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Using Sabermetrics for Fantasy Baseball: MiLB Stats

Once you've grown accustomed to having advanced tools to help make fantasy decisions, it can feel disorientating to be without them. Prospects are increasingly becoming a focal point in both real and fantasy baseball, but the minors simply do not have all of the data publically available for MLB players. For example, advanced plate discipline stats, Pitch Info, and anything Statcast-related are all currently unavailable for minor league campaigns.

Does this mean we go back to looking at ERA and batting average as the only indicators of future performance? Of course not! Instead, we do our best to work with what we have. The process begins by looking at the environment. Higher levels of competition result in more accurate data, so you should start by excluding anything lower than Double-A if a player's track record allows it.

Here's how to effectively use this data to give you an edge in your fantasy baseball league throughout the season.


In Leagues Of Their Own

The first point to remember is that the baseline for certain predictive metrics is different on the farm. Mike Podhorzer of had an excellent article detailing the specifics in 2017. For example, Double-A hitters collectively posted a .306 BABIP that year, while their Triple-A counterparts managed a .317 figure. Both marks are significantly higher than the MLB average, making a performance that looks fluky league-average.

Another common sticking point is IFFB%. Double-A batters posted a ludicrous 21.6% IFFB% on their fly balls in 2017, while their Triple-A counterparts were only slightly better (20.8%). This leads many fantasy owners to conclude that EVERY minor league prospect has a massive pop-up problem, but this is not the case. The stat is calculated differently on the farm, and you need to halve it to get something approaching an MLB projection.

Like MLB, each minor league and ballpark also has its own unique quirks and tendencies. For example, the Pacific Coast League is a Triple-A league notorious for inflating offensive statistics. If you want minor league ballpark factors, Baseball America posted them for 2019 here. If you want three-year factors, posted them for AA and AAA for 2014-2016.

If memorizing each league's tendencies is too overwhelming for you, you can look at Weighted Runs Created Plus (wRC+) as a shortcut. This metric sets 100 as the league's average offensive output, with each number higher or lower representing a one percent difference in either direction. This means that a wRC+ of 95 is five percent worse than league average, while a mark of 110 is 10 percent better. While the formula does not directly translate to fantasy value, park and league adjustments are already included in the calculation.


Forecasting MiLB Performance

Another common problem with minor league statistics is sample size. It is easier to run an unsustainable BABIP in a small sample than a larger one. The minor leagues compound this problem by allowing a healthy player to be called up or demoted multiple times in one season, leaving us with two or more partial season samples instead of one full season of statistics.

Due to the small sample, metrics such as BABIP can be unreliable for minor league players. In this situation, it's advisable to examine the player's plate discipline numbers and batted ball distribution (GB% vs. FB%) because they stabilize (or become predictive) more quickly. We don't have the additional information provided by metrics such as O-Swing%, but these metrics are still a good way to start MiLB analysis.

For example, Peter Alonso of the Mets organization received 273 PAs at Double-A (.314/.440/.573 with 15 HR) and 301 PAs at Triple-A (.260/355/.585 with 21 HR) in 2018. He hit a lot of fly balls (44.2% FB% at Double-A, 40.4% at Triple-A) with authority (HR/FB rates of 20.5% and 28.4%, respectively) while walking enough (15.8% BB% at Double-A, 11% at Triple-A) to project for a reasonable average.

With the benefit of hindsight, we know that Alonso was a tremendous fantasy asset last season. He slashed an impressive .260/.358/.583 with 53 HR in 693 PAs, posting the elevated FB% (41.5%), HR/FB (30.6%), and BB% (10.4%) that his minor league resume suggested.

Stealing bases is easier in the minors, but elite success rates are still something to look for when projecting fast players. Age is also a factor for minor leaguers, as a 28-year-old dominating a bunch of teenagers at Rookie ball isn't really that impressive.



To conclude, the fact that we do not know a minor leaguer's average airborne exit velocity or BABIP on ground balls does not prevent us from analyzing minor league players for fantasy purposes. We have tools such as FB% and BB% for hitters and FIP and LOB% for pitchers. We can still place these numbers into context by examining any given league's tendencies. Finding rookie breakouts before they happen is still challenging, but that's what makes it a worthy endeavor. Check out this link to learn more about how to apply advanced stats within a fantasy context.

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Using Sabermetrics for Fantasy Baseball: Expected Stats

Statcast is a valuable tool for fantasy analysis, and it can be easy to look at a stat called "Expected Batting Average" and blindly use it as your projection moving forward. Of course, proper use of these metrics is a little bit more nuanced than that.

First, a disclaimer: This article is about the "Expected Stats" found on Baseball Savant. It is not about the various "xStats" developed by fantasy analysts such as Mike Podhorzer or used in projection systems such as Ariel Cohen's ATC. Those tools have value, but any attempt at an in-depth analysis of them would involve far more math than the average fantasy owner is interested in.

With that out of the way, let's begin by identifying what the Expected Metrics are and how they work.


How To Use Statcast's Expected Metrics In Fantasy

The first is xBA, or Expected Batting Average. This statistic is calculated using Hit Probability, itself a stat measuring how often a batted ball with a particular exit velocity and launch angle has fallen in for a hit since Statcast was introduced in 2015. For example, a line drive to the outfield that has historically fallen in for a hit 80 percent of the time counts as 80% of a hit by Hit Probability. xBA is simply a batting average produced using Hit Probability, actual K%, and official ABs. If you play in a traditional 5X5 roto league, this is the Expected Stat you'll probably use the most.

As of January 2019, the Hit Probability formula was modified to include the batter's Statcast Sprint Speed, more accurately representing his ability to beat out a ground ball. That said, the adjustment feels like it may be too light in certain circumstances, so you may still want to make a slight adjustment upward for true jackrabbits.

Next up is Expected Slugging Percentage, or xSLG. It is calculated in the same manner as xBA, except that each batted ball is weighted according to its probability of being a single, double, triple, or home run instead of just a hit. If your league counts slugging percentage, you might get good use out of this stat.

Finally, we have Expected Weighted On Base Average, or xwOBA. It is calculated the same way xSLG is, except real-world walks and HBP are added to the equation. Each result is also assigned a linear weight with more math than the simple multiplication used to calculate slugging percentage. This is the stat with the most real-world value, but doesn't translate that well to fantasy unless you play in a realistic Points format.

The principal value of all three metrics is to take both luck and defense (and therefore actual results) out of the picture, allowing a player to be judged solely on his contact quality.

We'll assume that you play 5x5 roto and stick with the simpler xBA from here on out. Generally speaking, a player who posts a higher xBA than actual batting average would be expected to improve his average moving forward, while the opposite is true if a player's batting average is higher than his xBA.

Baseball Savant's Leaderboards allow you to sort players by the difference between their BA and xBA, so finding some samples is easy. Fernando Tatis Jr. of the San Diego Padres had the largest negative differential in 2019, posting a .317 average against an xBA of just .259. Tatis probably figures to beat his xBA based on the speed caveat noted above, but it is something to think about before spending a top draft choice on him in redraft formats.

Going the other way, Marcell Ozuna posted the best positive differential with a .288 xBA against a .241 actual mark. These advanced stats don't understand that certain players are more susceptible than others to the shift, so you should check those numbers before you blindly project improvement. In Ozuna's case, he performed roughly as well against the shift (.255 average) as he did without it (.258), so he looks like a nice bounceback candidate.

Pitchers illustrate another problem with xBA. Zach Plesac of the Cleveland Indians was the "luckiest" pitcher according to the metric in 2019, posting an xBA of .288 despite a batting average against of .241. The metric doesn't consider a defense behind a pitcher, however, so Francisco Lindor's outstanding 11 Outs Above Average could help Plesac sustain such a gap moving forward.

League-wide, major leaguers posted a .252 batting average and .250 xBA in 201, a two-point differential that has declined in each year of Statcast's existence. This trend suggests that the technology is getting better, but also that it isn't foolproof. It is always best to utilize Statcast Expected Stats as part of a broader analysis, rather than using them as your sole data point.



In summation, Expected Stats allow you to evaluate a player's performance based on his exit velocity and launch angle, taking variables such as the opposing defense out of the calculus. This can give you a better sense of a player's true talent level, but there are limitations on what you can do with it. Check out this link to brush up on other metrics you can use in your fantasy draft prep.

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Using Sabermetrics for Fantasy Baseball: Pitcher Statcast

Statcast metrics such as Barrels and Brls/BBE are great ways to evaluate a batter's performance, so it is only natural to assume that the metrics would be predictive for pitchers as well. As much as batters want to hit a Barrel every time, pitchers want to avoid them at all costs. Yet there is evidence that pitchers do not have the same influence over Barrels as a batter does.

Jorge Soler of the Kansas City Royals finished with a league-leading 70 Barrels hit last year. Mike Leake led MLB pitchers by allowing 59, a significantly lower number than Soler's total. Neither performance was an outlier, so it seems to take fewer Barrels to lead pitchers in Barrels given up than it does to lead hitters in Barrels hit. This fits well with DIPS theory, which states that batters can do more to influence batted balls than pitchers can.

It's also not fantasy-relevant, as Mike Leake just isn't that appealing a fantasy option. The nest five names on the leaderboard consist of names with a wide range of fantasy viability: Rick Porcello (55), Merrill Kelly (53), Madison Bumgarner (53), Patrick Corbin (49), and Shane Bieber (48). There isn't a compelling reason to group these guys together for fantasy purposes. Are these numbers indicative of anything?


How to Interpret Statcast Contact Quality Allowed

Bieber is by far the biggest name on the list above, so let's focus our analysis on him. He allowed his 48 Barrels in 554 batted ball events for a rate of Brls/BBE of 8.7% last season. Back in 2018, Bieber allowed 24 Barrels in 342 batted balls for a Brls/BBE of 7%. These metrics completely contradict the popular perception of him, as he was hit hard in 2018 (4.55 ERA despite 3.30 xFIP) before living up to his peripherals last season (3.28 ERA, 3.23 xFIP). His Statcast metrics failed to capture his fantasy line.

Using the Brls/BBE leaderboard might seem like a better bet than raw Barrel totals, but again we find a contradictory example within the top five. David Hess (13.2% Brls/BBE), Jeff Hoffman (12.9%), Erik Swanson (12.3%), and Derek Holland (12.2%) are all obvious fantasy avoids, but Josh Hader (12.6%) is the first RP off of the board in most drafts.

Hader had contributed obscene amounts of strikeouts, saves, and an ERA that starts with a 2 for two straight seasons now. His rate of Brls/BBE was slightly elevated in 2018 as well (10.6%), but there's no reason to think that it's predictive of a drop-off considering that Hader has already succeeded with it twice.

Maybe we need to simplify this and just use average airborne exit velocity? Unfortunately, the leaderboard in average airborne exit velocity is a total dumpster fire: Hess (96 mph), Chad Bettis (95.9 mph), Chad Green (95.6 mph), Felix Hernandez (95.5 mph), and Edwin Jackson (95.4 mph). We don't need Statcast to figure out that we really don't want to roster these guys, making it superfluous at best.



Ultimately, Statcast metrics such as Barrels and average airborne exit velocity should probably just be ignored for pitcher analysis. These metrics are great for evaluating batters, but I can't get anything out of them for pitchers even with the benefit of hindsight.

That conclusion may make this seem like a worthless article, but it isn't. Seemingly every fantasy analyst uses contact quality to credit or penalize pitchers, either through the Statcast numbers above or an approximation such as the Hard% posted on FanGraphs. This type of analysis may explain a pitcher's performance after the fact, but it seems to have zero predictive value. Therefore, there may be a competitive advantage to be gained by ignoring this type of analysis completely. Check out this link if you want to learn about some more predictive metrics.

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Finding Combo-Player Values Using Z-Scores and ATC Projections

Towards the end of last season, I asked the question – “Draft Speed or Pound the Power?” Loaded in this seemingly simple query are two contradictory approaches – one for power and one for speed.

  • Power Approach 1: Home run totals are dramatically up in baseball these days. Therefore, there are many power bats available late in drafts, and one does not need to purchase power early on.
  • Power Approach 2: Home run totals are dramatically up in baseball these days. Therefore, fantasy teams need to acquire tons of power early on in drafts to keep pace with the inflated team HR totals.
  • Speed Approach 1: Stolen bases are dramatically down in baseball these days. Therefore, one does not need to purchase speed early on, since team SB totals will be depressed.
  • Speed Approach 2: Stolen bases are dramatically down in baseball these days. Therefore, purchasing large SB players will make a tremendous difference for fantasy teams; they are essential to purchase early.

These seemingly contradictory philosophies beg the notion of acquiring as many “combo” players as a fantasy owner can possibly afford. In my article, I showed that multi-category players are a fantastic investment for one’s fantasy team. Finding the combo players is an exercise worth undertaking.

By most people’s standards, a “combo” player is defined loosely as a player who will hit a lofty number of home runs, and at the same time will steal a large number of bases. A 25/15 player refers to a hitter who will amass 25 HRs and 15 SBs. For many, the 25/15 individual power/speed thresholds are one way to define a combo player. Today, for my very first article on RotoBaller, I will take a look at finding this year’s combo players from the perspective of Z-Scores.



For those of you who have never used Z-Scores before, here is a brief introduction.

Z-Scores, often referred to as standard scores, are the kernel of a widely popular auction valuation method for fantasy. In a standard rotisserie baseball league, there are five scoring categories for hitters: BA, R, RBI, HR, SB. The question becomes – how do we combine all five categories into one all-encompassing metric? How do we know how many runs scored are equivalent to one stolen base, etc.?

The general idea involves transforming each categorical statistic in order to be on the same basis. The heart of the Z-Score engine calculates the following value for each player, by scoring statistic.

Where: Z[i] = Player i’s Z-Score;  X[i] = Player i’s Category Stat;  X-Bar = Average Stat for the category;  S = Standard Deviation for that category.

For all rate stats (beyond the scope of this article), we must first convert them into a counting stat. Using hits as an example (zxH), we calculate the total number of a player’s hits above the pool’s mean batting average.

Using the formula above, to calculate your player/category’s Z-Score: Take your player’s stat, subtract the average stat, and divide by the standard deviation across the player pool for the stat. Repeat for all categories. To obtain a player’s total Z-Score, simply sum up across all scoring components.

With regards to the individual Z-Scores as calculated above, a Z-Score of exactly zero indicates that a player is exactly average. A +1.00 indicates that a player is one standard deviation over the mean, and a -1.00 indicates that a player is one standard deviation below the mean.


Finding Combo Players

Now that we have set up the Z-Score framework, we can now look for “combo” players using these standard scores. Perhaps, one might define a combo player as having four categories with a Z-Score of at least +0.75. Or perhaps, one might choose to define combo as any 3 categories which have at least a +0.50 Z-Score.

Rather than set a hard definition for the number of categories requiring a particular threshold, I would like to use these Z-Scores as a means in order to filter for players. Let’s use the standard scores to scope out the players who are:

  • Excellent in every category
  • Great in every category
  • Good in every category
  • Excellent in most categories
  • Great in most categories
  • Good in most categories
  • Excellent in some categories
  • Etc.

The data used in this analysis stems from the ATC Projections as of February 16, 2020. Average Draft Position (ADP) data comes from the NFBC for the dates between 2/4/20 – 2/16/20.

This will be a meaningful discovery exercise. By filtering on various Z-Score thresholds, we will be able to find all of the combo players both atop the draft as well as lower down. We may be able to find some undervalued players who will be able to quickly balance out your rotisserie team’s categories.

Let’s start with the elite.


5 Categories with Z-Scores over +1.00

In most standard rotisserie leagues, Christian Yelich will be taken this season either first, second or third. Some will debate that Mike Trout should be taken with the first overall draft selection since he is the most stable player in all of baseball. Others will debate that Acuna should be selected first due to his potential 40/40 ability. Either way, Yelich is a consensus top 3 draft pick in 2020.

What makes Yelich special is that he is the only player to have a Z-Score of at least +1.00 in each and every category. Yelich is more than one standard deviation better than the mean in every offensive category; he is the definition of a true 5-category player. Yelich will set an incredibly strong base for his fantasy owners lucky enough to draft him.


5 Categories with Z-Scores over +0.75

Next up, let’s add in four more players who have Z-Scores in each category of at least +0.75.

Trout would have made the prior list, if not for his mere 14 stolen base projection which only earned him only a +0.84 in that category. Acuna makes the SB threshold by a wide margin but falls a hair short of making it in the batting average category. Similar to Acuna, Trevor Story nearly misses the opening group’s cut by just a few points of average.

Francisco Lindor almost made the elite group, but for his +0.82 in RBI. As the runs batted in category is very context dependent, Lindor has the raw skills to be among the elite.

If you are not fortunate enough to select among the first three players of drafts, have no fear – there are two shortstops later in round one that have near-elite skills in Story and Lindor.


5 Categories with Z-Scores over +0.50

Next, we will look at 6 more players who are should be considered strong combo players. All six have at least a +0.50 Z-Score in each and every scoring category.

Similar to Mike Trout, Cody Bellinger misses elite status because of his stolen base projection. In Bellinger’s case, he is just a small decimal point away from making the second combo tier. Mookie Betts, another early first-round player, misses the first two lists because of a lower projected RBI total. Four categories of at least +1.00 Z-Scores and an 85 RBI projection though, is not too shabby!

Jose Ramirez is the only second-rounder to make the 5-category combo player cut. ATC projections are expecting him to revert back to his 2017-2018 days where he hit 30+ homers and stole 25+ bases. ATC is also expecting Ramirez to still hit for a valuable average at .276. If you miss out on Story/Lindor in the first round, Ramirez might be a wonderful 2nd round consolation.

At ADPs just under 40, we have Javier Baez and the young Austin Meadows. Both are projected for 31 HRs and 13-14 SBs. Baez projects for slightly more run production, but Meadows will give you a few more points of batting average. Take note of the two during the 4th round of your drafts.

But the player that truly grabs my attention here is none other than Keston Hiura. In his second season, Hiura is expected to achieve a Z-Score of at least +0.50 in all categories. His greatest Z-Score maxes out at +0.68, making him a true “many paths to value” hitter. I love these types of players – who don’t do anything exceptionally well, yet do decently well in all categories. Hiura reminds me of players like Alex Gordon, Alexei Ramirez and Hunter Pence. I used to love grabbing these types on my roto teams year after year.

Hiura’s price isn’t cheap this year, but his categorical risk is lower than most due to his "combo" nature.

Next up are the 4-category combo players.


4 Categories with Z-Scores over +1.00

Note - Even though the 5-category players belong on the 4-category lists, I won’t repeat any names we have seen thus far. I will only be listing out the new members to each group.

You will now notice that a few numbers above are colored in red. The red colors signify players/categories which have a below average Z-Score. You will also notice that for each of the players in this tier, the one category below the +1.00 threshold is always stolen bases. Furthermore, for almost all of the players (other than Juan Soto), their Z-Score for SB are negative.

All of these players are currently being selected in the first two rounds of drafts. Soto, Arenado and Bregman are currently first round players. Freeman is going near the 1-2 turn of 15-team drafts. Devers and J.D. Martinez can be found in the back half of the 2nd round.

To me, Devers is the sharpest pick of the lot. His stolen base total is close to average. His power is superb, and for all other categories, he is extraordinary. Devers has over a +1.80 projected Z-Score in three different scoring categories. He’s a sneaky late 2nd round pick as a 4-category combo player.


4 Categories with Z-Scores over +0.75

Dropping the Z-Score threshold to +0.75 yields two more players – Rendon and Alvarez. Rendon had nearly made the previous list if not for his good-yet-not-elite power totals. 29 HRs these days is now merely “very good.”

Yordan Alvarez, who has a higher Total Z-Score than Rendon is being selected in drafts 20 picks later. Perhaps fantasy owners are discounting him because of his sophomore status? Perhaps he is discounted because he is DH-eligible only? Whatever the reason, ATC projections think that he is a relative bargain as a 4-category combo player.


4 Categories with Z-Scores over +0.50

For this tier, we now relax the 4-category requirement of Z-Scores to +0.50.

Rather than going though all of the above players, a few notes:

  • Bryce Harper is the only player to make this combo list due to his stolen bases and not his batting average. Harper has a below average BA.
  • Eddie Rosario and Nick Castellanos are the only two players found after pick 75. Rosario is especially interesting, as his overall Z-Score total is in line with others selected 25-40 spots ahead of him.
  • George Springer is statistically similar to Rafael Devers. Unless you believe that he is due for a banging or buzzer scandal related decline, he’s a great choice at his price point.

Next up are the 3-category combo players.


3 Categories with Z-Scores over +1.00

Now we come to the more limited combo environment, where any 3 categories will do. For our first 3-category level, each player must still achieve elite status in three different categories.

Some quick notes on these batters:

  • Trea Turner is this tier’s first-round player. His power metrics are lacking for his price point, but his speed more than makes up for it. He is somewhat riskier than other elite options as he is only a 3-category contributor.
  • Ozzie Albies is above average in all five categories (all Z-Scores > 0.00). Like Hiura above, I would deem him as a “many paths to value” player. Second base is not an especially deep position in 2020 - so I enjoy the idea of drafting one of Albies/Hiura, which sets a nice base in the middle infield.
  • Matt Chapman is available at pick 89. For elite power and run production, he’s a great option for the price.


3 Categories with Z-Scores over +0.75

  • Michael Conforto is a name that I didn’t expect to see in this study. But as a Mets fan, I am pleasantly surprised! Tuck Michael’s name away in case you need a 3-combo player after pick 110.
  • Rhys Hoskins is also a player available late in this group. He has more power than Conforto but could hurt your batting average and speed. I prefer Conforto to Hoskins.
  • Max Muncy appears overvalued in this tier. Simply compare his Z-Score profile to Hoskins to see that a 50-pick gap isn’t worth reaching for.


3 Categories with Z-Scores over +0.50

  • Josh Bell, Tim Anderson and Trey Mancini are the players found near pick 100. The trio appear undervalued according to ATC’s projected Z-Scores.
  • Batting average darling Michael Brantley is included in this group. That batting average is a rare commodity after pick 125.
  • Later picks in the tier include Carlos Santana, Paul DeJong and Adam Eaton. Eaton is an immense bargain if he can stay on the field all season long. DeJong at pick 191 is a great value.

Finally, let’s look at the players who are strong in just 2 categories. We aren’t looking at “combo” players any longer, but it is helpful to know the players who can provide a strong boost for particular categories.


2 Categories with Z-Scores over +1.00

  • Altuve and LeMahieu will help in runs and batting average.
  • The rest of this group will help in HRs and RBI.
  • Khris Davis currently sits at pick 177. He projects for at least a +1.25 Z-Score in both the power and RBI categories. Keep Davis in mind for power late, provided that your team has built up enough speed and batting average.


2 Categories with Z-Scores over +0.75

Just for kicks, here is one more listing for players who are “very good” at 2 categories:

  • Kevin Newman/Nick Madrigal are late average/speed helpers. Newman has a job in the majors; Madrigal should come up to the big league at some point.
  • Yuli Gurriel projects to be an undervalued BA source.
  • Franmil Reyes appears to be an undervalued power source.
  • Vlad Jr. seems like the overspend in this group.
  • Jeff McNeil is not far away from being a “many paths to value player.”
  • Carlos Correa projects to be above average in four categories.
  • Marcell Ozuna projects to be above average in five categories! That is quite valuable around pick 100.



Fantasy owners often do single category filtering in-draft. Instead, we can better prepare ourselves by parsing out the combo players in advance. By mapping out the multi-category contributors via Z-Scores, we are able to bubble up a number of potentially helpful players for the upcoming 2020 draft season.

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Using Sabermetrics for Fantasy Baseball: Spin Rate

Spin rate has become one of the most recognizable Statcast metrics, with supporters of a given pitcher highlighting his spin rates to make their case.

Unfortunately, the baseball world has done a lousy job conveying what spin rate really means. The result has been a ton of owners who know that spin rate exists, but very few who can use it to improve their fantasy rosters.

This article will teach you everything you need to know to fold spin rate into your pitcher evaluations. We'll also illustrate the efficacy of spin rate using Pitch Info data from actual pitchers. Let's get started!


How to Interpret Spin Rate

Spin rate is measured in RPMs, or Rotations Per Minute. Each pitch type has its own baseline numbers, so a high-spin fastball might have an average spin rate for a curve. Comparing different types of pitches by spin rate is rather pointless, so try to focus on how any given pitcher's offering compares to the same pitch type thrown by other arms.

So, are higher or lower spin rates better? The answer is that it depends on the type of pitch you're looking at. Let's start with fastballs.


Interpreting a Fastball's Spin Rate

The average spin rate for fastballs ranges from 2,100 RPM to 2,400 RPM. Heaters with spin rates above this range tend to have "late-life" and induce more whiffs than your average heater. They usually have backspin, or spin against gravity, that guides the ball weakly into the air if contact is made. This allows them to post elevated pop-up rates to complement their whiffs. It's worth noting that fastball spin rate is positively correlated with velocity, meaning that a pitcher with a velocity spike may also experience a spin rate jump.

For example, Mike Minor's four-seam fastball averaged 2,650 RPM in 2019 to lead all MLB starters. Its 9.4 SwStr% was very good for a heater, so he got the whiffs we would expect from a high spin rate. It also had a distinct fly ball tendency when put into play (41.5% FB%) and a high IFFB% (28.9%), suggesting that it produces pop-ups as expected as well. Minor's fastball is clearly a weapon.

However, Minor does not possess the best four-seamer in MLB despite pacing the pack in spin rate. Justin Verlander's average fastball spin rate of 2,574 RPM ranked 15th in MLB (minimum 250 total pitches thrown), but bests Minor's in all of the metrics cited above: 14.3 SwStr%, 55.6 FB%, 25.5 IFFB%. The reason why is that Verlander's fastball gets more movement out of its spin than Minor's.

We have to consider "gyrospin," alternatively called "useless spin." If you've ever seen a bullet in slow-motion, it rotates slightly while flying straight to its target. That rotation is gyrospin and it has no impact on where the bullet ends up. A metric called "Active Spin" measures how much spin is actually affecting a ball's trajectory. Verlander paced baseball with an Active Spin rate of 98.5% on his fastball, while Minor's 67.8% Active Spin rate was much less impressive.

If you're looking for a contact manager instead of a strikeout artist, you want a spin rate below the average range above. Low-spin fastballs produce weakly-hit ground balls and a lower slugging percentage against compared to their high-spin counterparts. However, this can be a dangerous way to live. Contact managers need a lot of things outside of their control to go right to become fantasy assets, so fantasy owners should look for high-spin strikeout artists whenever possible.


Evaluating Spin Rate on Secondary Offerings

Unlike fastballs, changeups usually want a low spin rate to maximize how much they move. For instance, a changeup is Anibal Sanchez's out pitch. Last season, it posted an 18.7% SwStr% and 51.4% chase rate, suggesting that opposing batters had no idea where it was going to end up. The reason why is its spin rate: it averaged 1,447 RPM last year.

Breaking pitches usually want high spin rates. Unlike fastballs, breaking offerings have topspin, or spin toward the ground, that can help guide the ball downward if contact is made. Breaking pitches tend to be a given pitcher's strikeout pitch though, so owners generally aren't looking for any kind of contact on them. Breaking ball spin rates are therefore the least important to look at but may provide interesting information at times.

There are enough variables in play here that spin rate should never be considered on its own. Instead, start with Pitch Info and then use spin rate to confirm if a given pitch can sustain its elite performance (such as Verlander's four-seamer) or if it was probably a fluke.



To summarize, spin rate is measured in RPM. Fastballs can be good with high or low spin rates, but higher spin rates tend to translate better to fantasy. Changeups want as little spin as possible to maximize their movement. Breaking pitches typically benefit from higher spin rates, but it's not as clear-cut as it is for fastballs and changeups. Finally, gyrospin can distort spin rate readings, meaning that you should always combine spin rate with other metrics in your analysis. Check out this link to learn how to apply other advanced metrics to your fantasy prep.

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Using Sabermetrics for Fantasy Baseball: Pitch Info

One of the most fundamental questions in fantasy sports is if a player's current performance is sustainable. More than any other sport, baseball has a slew of statistical measures that can be dissected in numerous ways to analyze player performance.

Pitch Info is a publicly-available pitch tracking system that provides a lot of different data to help fantasy owners make this determination for mound breakouts and busts alike.

Let's look at how to effectively use this data to give you an edge in your fantasy baseball league throughout the season.


Interpreting Pitch Info Data: Velocity

The first data point to understand is velocity. Generally speaking, a pitcher that loses fastball velocity is losing something to either an undisclosed injury or the aging process. Pitchers that gain velocity can expect to increase their production. For example, Lucas Giolito saw his average fastball velocity spike to 94.6 mph last season (92.8 mph in 2018). His K% soared as a result (32.3 K% vs. just 16.1% in 2018), making him a fantasy stud (3.41 ERA) instead of a dumpster fire (6.13 ERA in '18)

When evaluating a pitcher's velocity, you should always look at his baseline velocity as opposed to an arbitrary league average. Giolito's 94.6 mph isn't all that impressive by modern standards, but it clearly allowed him to take his game to a new level. Other variables like movement and location also matter, but velocity is a good introduction to using Pitch Info data.


Interpreting Pitch Info Data: Pitch Mix

Slightly more advanced is pitch mix, or what pitches a pitcher throws and how often he throws them. A pitcher may improve his production by abandoning a poor pitch or developing a new, effective one. This is a good stat to consult if a pitcher sees a sharp change in his K%, as a change in pitch mix could represent the change in approach that supports the new number. If the change does not have a corresponding pitch mix shift, it may be less sustainable.

Let's return to Giolito as our example. He made two substantial repertoire changes as part of his breakout, and Pitch Info allows us to track the performance of each individual pitch. First, he eliminated his sinker (19.9% thrown in '18. 0.1% last year) in favor of his four-seamer (39.5% in '18, 54.9% last year). Giolito's four-seamer was amazing, generating an above-average number of whiffs (11.5 SwStr%) while still spending enough time in the zone to get ahead in the count (55 Zone%). His sinker generated very few swings and misses in 2018 (4.3 SwStr%), while its 53.9 Zone% was just shy of his new heater. This was a good change.

Second, he threw more changeups (15.3% to 26.1%) at the expense of his curve (10.1% to 4.2%). Giolito's change was one of the best pitches in baseball last season, producing an incredible 22.2 SwStr% with a 52.5 Zone%. Throwing more of it could only be a good thing. In contrast, his curve doesn't accomplish anything in particular, with pedestrian SwStr% (5.1), Zone% (36.8), and O-Swing% (18.9) rates. Again, this change figures to improve Giolito's fantasy stock.

The same type of analysis may be performed for a number of other stats, including BABIP, FB%, LD%, GB%, and HR/FB. There is no point in looking at a league-average pitch mix, as every pitcher owns a different arsenal. All of these variables may be considered over a pitcher's complete repertoire to determine how good he is (or should be) without relying on any conventional metrics. This can be good for identifying sleepers, as pitchers that have one or two standout pitches could break out by simply using them more often.


Interpreting Pitch Info Data: Pitch Results

What is the baseline for this type of analysis? It depends on the observer, as there are almost as many ways to interpret this data as there are data points to consider. The league average O-Swing% was 31.6 in 2019, and most good wipeout pitches need to beat this number substantially. The overall Zone% was 41.8, including pitches like splitters in the dirt and high fastballs that were never intended as strikes.

The fastball will generally be inferior in results to pitches that do not need to live in the strike zone, as pitches hit outside of the zone offer better results than offerings in the hitting zone when they are put into play. However, getting ahead in the count is necessary to make those pitches work as intended, making (sometimes) mediocre fastball results a necessity.

It is dangerous to generalize, but 2-seam fastballs and sinkers tend to stink for fantasy purposes. They're usually hit harder than fastballs. They may post strong GB% rates, but also have high BABIPs and scary triple slash lines. Any sinker hit in the air was probably a mistake, so the HR/FB rate is usually high for the limited number of fly balls hit against them. Their SwStr% rates also tend to be poor. Overall, fantasy owners prefer a straight four-seamer to be the "zone pitch" in a pitcher's repertoire.

Personally, I look for fastball with a SwStr% of around 9% and a Zone% of at least 53%. Many pitchers succeed with a lower Zone%, but I can't stand watching walks. I then look for a wipeout pitch that offers a SwStr% of at least 17 and an O-Swing% of 40. Ideally, there is a secondary K pitch that prevents the 0-2 offering from being too predictable. Only aces really fulfill all of these criteria, but I can dream, right?



To conclude, Pitch Info tracks a lot of data of interest to fantasy owners, including average velocity, pitch mix, and individual pitch results. All of this data may be used to predict who will break out or which breakouts can sustain their current performance. If you would like more analytical tools to help you dominate your leagues in 2020, check out this link.

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Using Sabermetrics for Fantasy Baseball: Pitcher BABIP

While FIP is a useful tool to predict a pitcher's future ERA performance, fantasy owners should remember that ERA, not FIP, is what really matters in most formats. This means that we are interested in the "luck" that separates the two statistics.

While some of this luck is unpredictable, we can and should predict some of what goes into a pitcher's bottom line. BABIP plays a big role in the variation of a pitcher's perceived luck, but it may not be as clear-cut as it seems.

Let's get to it!


How to Interpret BABIP for Pitchers

When calculating BABIP for hitters, we assume a neutral defense because they figure to see a balance of poor and skilled defenders as they travel around the league. This is not true for pitchers, as they always pitch in front of their own club's defenders. A team with Victor Robles and his 23 Outs Above Average (or OAA) figures to provide better defense to its pitchers than a team that lacks a premium glover. A better defense helps pitchers sustainably outperform their FIP.

Outs Above Average is a Statcast metric that makes it easier to look at the quality of a pitcher's defense. Outs Above Average, or OAA, measures each player's defensive contributions using Catch Probability. If a batted ball is caught by a player, the player receives OAA credit equal to 1 - the ball's Catch Probability. For example, a successful catch on a ball with a 40% Catch Probability is worth 0.6 OAA (1 - 0.4 = 0.6).

Players also lose points equal to the batted ball's Catch Probability if they flub the catch. Missing the ball in the example above would subtract 0.4 from the player's OAA. Another great feature of OAA is that the stat is sortable based on a shift. If you want to know how a third baseman fares when shifted to a traditional shortstop position, OAA makes it easy to look at that data. Robles led all outfielders in OAA last season, while Javy Baez (19 OAA) took the top spot among infielders.

There are other defensive metrics, but they are much more abstract than OAA while also leaving out important pieces of the puzzle. Ultimate Zone Rating (or UZR) makes no effort to account for the shifts in today's game, rendering it completely obsolete. Defensive Runs Saved (DRS) has one fantasy purpose: measuring the value of a pitcher's defensive contributions to his own cause.

For example, Dallas Keuchel finished second among pitchers in DRS last season despite only throwing 112 2/3 IP. This is nothing new for Keuchel, who has a whopping 50 DRS over his 1,302 career IP. Fantasy owners have known for years that Keuchel posts lower than average BABIPs when he's on despite being a ground ball pitcher, but the reason isn't some magical contact suppression ability. It's the fact that Keuchel rates as roughly double the defender Javy Baez is if you prorate his DRS over a position player's number of innings.


What else impacts a pitcher's ERA?

BABIP is also partially determined by a pitcher's style. An extreme ground ball pitcher may have a higher BABIP against because grounders have higher BABIPs than fly balls (.236 to .118 in 2019.) This stylistic difference also changes how much a given pitcher will benefit from (or be hindered by) a particular defender on his team. For instance, a fly ball pitcher would love to pitch in front of Robles, while a ground ball specialist would benefit more from an elite infielder like Baez instead.

While defense is largely out of a pitcher's control, some pitchers can control their BABIP to a degree. For example, you would probably be tempted to say that the .218 BABIP Justin Verlander allowed last season was a fluke, and you would be partially right. However, Verlander combined a strong fly ball tendency (45.2 FB%) with an above-average IFFB% (12.4%). Flies have the lowest BABIP of all batted balls. Furthermore, pop-ups are rarely hits, so inducing them consistently enables a pitcher to post better than average BABIPs. The combination would be expected to produce a low BABIP allowed.

The same principle holds for pitchers who can limit line drives, but this skill is not quite as sticky as pop-ups. Liners post very high BABIPs but randomly fluctuate, as we have seen in a previous article.

Every pitcher allows a few hits, and the sequencing of these events may also cause a difference between a pitcher's FIP and ERA. Allowing three base hits over three innings is probably harmless, while allowing three hits in one inning and then nothing in the next two frames likely puts a run on the board.

Sequencing luck is measured by strand rate, or LOB%, and research shows that it is largely an unstable, luck-driven stat. In 2019, the league average LOB% was 72.3%, with higher numbers generally forecasting a higher ERA moving forward. Elite strikeout guys tend to be the best at getting the K "when they need it," and as such may sustain slightly elevated strand rates.



To conclude, a pitcher's BABIP includes some unknown variables but also some predictable inputs. The quality of his defense can help or hurt him. Sequencing does not affect BABIP, but can impact a pitcher's ERA substantially. A given pitcher's style, as a ground ball or fly ball specialist, may also impact his performance. If you would like to learn more about other advanced stats, check out this link for a full glossary.

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Pitchers Most Impacted By Defense: OAA Risers

Early in January, Statcast released a full fielding leaderboard of their Outs Above Average (OAA) metric, which had previously been limited to outfielders only. The metric takes into account fielder positioning, reaction time, throw difficulty, and batter speed in order to calculate how likely a play is to become an out. The more unlucky, the more points a fielder will get for converting the out and the fewer he'll lose if the play winds up becoming a hit.

There's a more detailed breakdown of it here, but essentially it aims to tell us just how good a fielder is and just how many potential outs he recorded over the norm.

Does this new metric have any impact on fantasy baseball? Perhaps we can find a way to evaluate the stickiness of the metric or see just how much we should be using it when projecting pitching value for a given season.


The Unlucky Ones  (Pitchers with Low OAA)

Now that all infielders have been charted on this metric what can be extrapolated out of that is just how much a certain pitcher benefitted from elite defense behind him. In their own words, Statcast can track "the performance of the defense behind the pitcher while he was on the mound." The higher the recorded OAA when a pitcher is on the mound, the more outs his defense recorded that likely shouldn't have been outs based on the average outcome. The lower the OAA is for a pitcher, the more his defense gave away outs that should have been converted on average.

This alone isn't enough to determine if a pitcher is good or bad or if their 2019 fantasy season was real or fluky; however, placed in a larger context, it can tell us which pitcher's stats may have been the result of their own changes, defensive skills, luck, or perhaps a grouping of the three.

Since this metric is relatively new when it comes to its impact on pitching, I decided to dig into a few names from the top and bottom of the leaderboard just to see if there was anything to take away. I'm thinking aloud here or, more precisely, typing as I go, so feel free to take a look at the Statcast Leaderboard yourself and see what stands out to you.


Michael Pineda (SP, MIN)

According to OAA, Michael Pineda was the pitcher most hurt by his infield defense with a -6% success rate added and a -8 total OAA. What's interesting is that Pineda has a career 42.7% GB rate but only allowed 36.1% last year as his LD% and FB% jumped two and eight points respectively from his last full seasons. So, overall Pineda didn't require his infield defense to do a lot for him. Part of this could be attributed to an increase in fastball usage and a decrease in sliders in his first year back from injury.

As Jeff Zimmerman has pointed out in his recent article on the way injury affects performance, it would be smart to assume Pineda goes back to matching his career levels now that he is another year removed from injury. With that, plus the addition of Josh Donaldson at 3B (18th in OAA), Pineda should at least duplicate his low 4s ERA, so I would take advantage of projection systems that seem to project him around 4.60.


Matthew Boyd (SP, DET)

Boyd is generally more of a flyball pitcher, but he saw his GB% rise in 2019 as he began to use his slider more. The poor defensive numbers behind him were likely impacted by Jeimer Candelario's injury, since he had a 3.4 UZR at 3B, while his replacement, Dawel Lugo, had a -2.7 UZR. However, there might not be much improvement in 2020. If Willi Castro wins the starting shortstop job, he will bring league-average defense to the position. The only issue with that is that Niko Goodrum had a 1.9 UZR at SS (17th in OAA) but a -.6 at 3B, where he will likely play if Castro breaks camp with the team. That will then leave Jonathan Schoop and his -3.8 UZR at 2B and CJ Cron and his -.6 UZR and 74th-ranked OAA at 1B. Boyd's OAA could regress a bit to the mean, which would lead to a slight improvement in his .307 BABIP, but his new infield defense isn't going to be an asset behind him and shouldn't suggest actionable growth for 2020.

On a side note about Schoop and where this OAA will be intriguing to keep diving into: Schoop ranks 29th overall on the OAA leaderboard, but a closer look at his individual breakdown shows that Schoop's best plays come when he is essentially shifted to play the normal position of shortstop. A lot of the plays he has to make when he's positioned as normal second baseman are decidedly average. It's fascinating and overwhelming at the same time. 


Masahiro Tanaka (SP, NYY)

Tanaka is a splitter/slider pitcher who accumulates near 50% groundballs in a season, so he relies on defense more than a lot of other arms. While his slider improved last year to a 20.3 pVAL, the splitter plummeted, perhaps because of the new baseball, to a -5.5 pVAL, the worst of his career by a large margin. The ineffectiveness caused him to rely more on a fastball that has never been a good pitch, and he saw his O-Contact rise 10% and his SwStr% drop to a career-low 10.7%.

In order for Tanaka to have any success, he needs to find that splitter again. Without getting batters to miss, he is allowing more contact in front of a defense that is clearly not helping him out. Luke Voit was a -3.9 UZR at first base; Gleyber Torres was a -4.2 at 2B and -2.1 at shortstop, while Gio Urshela was a -2.5 UZR at 3B and still way better than Miguel Andujar. If we're using OAA, Voit ranked 38th out of 40 qualified first baseman, Gleyber Torres ranked 129th out of all infielders, and Urshela ranked 75th. Simply put, Tanaka isn't going to get help from his defense, and if the splitter isn't biting hard in the spring, it's best to stay away from him in most drafts.


Jon Lester (SP, CHC)

Lester showing up on this list is interesting because we generally think of the Cubs' infield of Javier Baez, Anthony Rizzo, and Kris Bryant to be a solid defensive one. In fact, Baez was the best infield defender according to OAA, while Addison Russell was 30th once he came back and slotted in at 2B. Rizzo has a 3.7 UZR, but he appears as the 102nd infielder based on OAA, mostly due to underperformance on plays close to the line. Since Rizzo performed much better in the same metric in 2017 and 2018, it's reasonable to expect improvement defensively from him. So, on the surface, there is no reason for the Cubs to have a poor defense, and very few Cubs appear negatively impacted by bad defense, according to OAA.

Perhaps it's just an issue of Lester being unlucky? Subpar defense performance might also help explain why Lester came into the season with a career BABIP under .300 but registered a .347 BABIP in 2019 despite the same Contact%. Now, Lester isn't totally free of blame. He gave up more hard contact than any other year in his career, while also seeing a 3% jump in O-Swing% and 2.6% jump in O-Contact%, suggesting that he was unable to effectively put hitters away or get them to chase. While much of his poor season had to do with regression in skills, the OAA numbers paired with a rise in K% and a decline in BB% suggest that Lester may pitch closer to his 2019 xFIP of 4.35 than current projections, which have him over a 4.70 ERA.


Steven Matz (SP, NYM)

A step lower on the OAA leaderboard for pitchers with the least amount of defense help is Steven Matz. It'snot entirely surprising considering that the Mets had one of the worst infield defenses last year and figure to this year as well. Amed Rosario was the 122nd infielder based on OAA, while Peter Alonso was 132nd, and Robinson Cano was 91st. Jeff McNeil replacing Todd Frazier at 3B should help a little since McNeil had a 2.4 UZR in his limited reps at 3B last year, but the overall unit won't be a benefit to Matz. Although Matz saw a .034 jump in BABIP last year despite allowing fewer groundballs, he also saw both his O-Swing% and O-Contact% rise, like Lester, and his Z-Swing% and Z-Contact% as well.

In all, Matz gave up more contact, produced a below-average 9.6 SwStr%, and generated a lower K% than in 2018. His defense may not have helped him, but I wouldn't be banking on a major difference in 2020 as Matz still has the same defense and a discouraging set of underlying metrics.


Adrian Houser (SP, MIL)

Houser's presence on this list intrigues me because of how much more reliant he was on his defense in 2019 than years past. Although Houser's Major League K% jumped from 13.6% in an admittedly small 2018 sample size to 25.3% last year, he also saw his GB% jump from 39.5% to 53.4%, which is more in line with his minor league numbers. Historically, Houser has allowed a fair amount of contact, particularly medium contact, and that was impacted last year by the added emphasis on a sinker, which he threw 36% of the time. The pitch registered a 17.1 whiff%, which was by far the lowest of any of his pitches but had a .242 BAA and a .221 xBA. Houser is likely to see more of a correction to the xBA given a stronger defense behind him. Eric Sogard, currently penciled in at third base, was the 27th best infielder in OAA, which is an improvement over Travis Shaw's 70th ranking, and new first baseman Justin Smoak has a career 2.7 UZR at first base. Additionally, as Keston Hiura gets more comfortable at the Major League level, he's bound to improve on his 130th ranking in OAA.

While we shouldn't expect a massive jump from Houser, a better defense behind him and more regression to the league average with OAA should lead to fewer hits and a potentially lower ERA, likely signaling that last year's 3.60 xFIP and 3.72 ERA was no fluke. If he's given a full season in the Brewers' rotation, he could certainly prove valuable.

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Pitchers Most Impacted By Defense: OAA Fallers

Early in January, Statcast released a full fielding leaderboard of their Outs Above Average (OAA) metric, which had previously been limited to outfielders only. The metric takes into account fielder positioning, reaction time, throw difficulty, and batter speed in order to calculate how likely a play is to become an out. The more unlikely, the more points a fielder will get for converting the out and the fewer he'll lose if the play winds up becoming a hit.

There's a more detailed breakdown of it here, but essentially it aims to tell us just how good a fielder is and just how many potential outs he recorded over the norm.

Does this new metric have any impact on fantasy baseball? Perhaps we can find a way to evaluate the stickiness of the metric or see just how much we should be using it when projecting pitching value for a given season.


The Lucky Ones (Pitchers with High OAA)

Now that all infielders have been charted on this metric what can be extrapolated out of that is just how much a certain pitcher benefitted from elite defense behind him. In their own words, Statcast can track "the performance of the defense behind the pitcher while he was on the mound." The higher the recorded OAA when a pitcher is on the mound, the more outs his defense recorded that likely shouldn't have been outs based on the average outcome. The lower the OAA is for a pitcher, the more his defense gave away outs that should have been converted on average.

This alone isn't enough to determine if a pitcher is good or bad or if their 2019 fantasy season was real or fluky; however, placed in a larger context, it can tell us which pitcher's stats may have been the result of their own changes, defensive skills, luck, or perhaps a grouping of the three.

Since this metric is relatively new when it comes to its impact on pitching, I decided to dig into a few names from the top and bottom of the leaderboard just to see if there was anything to take away. I'm thinking aloud here or, more precisely, typing as I go, so feel free to take a look at the Statcast Leaderboard yourself and see what stands out to you.


Dakota Hudson (SP, STL)

No pitcher was helped more by his defense in 2019 than Hudson. When he was on the mound, the defense recorded 16 OAA, which was double the next highest mark in the league. Since that second-highest mark was set while teammate Miles Mikolas was on the mound, it's safe to say that the Cardinals infield performed well last year. In fact, if you look at the OAA leaderboard, Paul DeJong finished seventh overall, Kolten Wong finished 13th, Matt Carpenter was 25th, and Paul Goldschmidt was 33rd. If Carpenter's myriad injuries were to act up, Tommy Edman would be only a slight downgrade, as he finished 48th overall. Hudson's extreme groundball tendencies - 56.9% - and low SwStr% - 9.8%- make him overly reliant on his defense, so when he gets well above average help like he did last year, he can succeed.

However, regression is coming. Not necessarily because the defense will get worse behind him, but because he's a clear outlier in OAA. If he even gets the defense his teammate Mikolas did, it would negatively impact his overall numbers. The fluky nature of Hudson's season is also backed up by his 3.35 ERA despite a  4.55 xFIP,  his relatively low .274 BABIP and high 40% Hard Hit%. They all suggest that tougher days are ahead.


Merrill Kelly (SP, ARI)

In his first year back stateside after a turn in Japan, Merrill Kelly had some intriguing runs of starts where he showed a plus fastball and the ability to stifle an offense. However, OAA suggests that he was fortunate in the modest success that he had, and the defense behind him in 2020 may have taken a step backward.

With the addition of Starling Marte, Ketel Marte has been shifted to 2B permanently, which is great for the Diamondbacks offense, but worse for the defense as Ketel Marte had a 5.6 UZR in the outfield but only a -1.3 at 2B. Also, a look at Kelly's contact profile hints at possible regression. Kelly's 85.1 Z-Contact% and minuscule 12.7% soft contact suggest that batters are frequently touching him up when he's in the zone. Without the same level of defense behind him, he could be looking at a worse season, so it's best to tread carefully in drafts.


Lance Lynn (SP, TEX)

Lance Lynn is an interesting case. He benefited from the fourth-highest OAA in the league, which indicates that defense played a role in his success; however, Lynn's GB% dropped over nine points and his SwStr% rose 2.5 points as he dialed back on his sinker. The new pitch mix led to decreases in both O-Contact and Z-Contact and a 5.1% increase in K%, which simply means that batters are making less contact against Lynn and he needs his defense's help less than in year's past. However, it's foolish to think that the defense provided no benefit.

Almost half Lynn's OAA seems to have come due to outs above average laterally towards 3B. That could be because of Elvis Andrus' defense, which got him ranked 34th overall on the OAA leaderboard, but it also could be because of the defense of Asdrubal Cabrera, who played 93 games at 3B for Texas and had a UZR of 3.4. When he was traded, Danny Santana (0 UZR) and Nick Solak (-3.2 UZR) saw the majority of the innings. This season, it appears likely that Solak or Todd Frazier (-1.2 UZR) would see the majority of innings at 3B, which could lead to weaker overall defense for Lynn. I just don't think he was as reliant on these defensive metrics as some of the other names on this list, so I wouldn't expect it to impact his final stats too much.


German Marquez (SP, COL)

Marquez's presence on this list isn't surprising. He's changed his pitch mix, adding more sliders and sinkers and relying less on his fastball, in order to increase his GB% each year of the last three years as a way to balance the effect of Coors Field's altitude. There are also four Rockies in the top 30 of the OAA pitcher leaderboard - tied for the most of any team. In fact, Nolan Arenado ranked second overall in the OAA leaderboard, while Trevor Story finished a few places behind in fifth. Both players also registered elite UZR numbers - Nolan Arenado finishing at 10.3 and Trevor Story at 8.6.

Despite the above-average defense, Marquez's season-long numbers still suffer due to playing his home games at Coors, where he's been unable to prevent batters from lifting his pitches. His home/road splits last year are awful. 3.67 ERA on a .212 BAA and .273 wOBA on the road, and a 6.26 ERA on a .317 BAA and .356 wOBA at home. If the Rockies' fractured relationship with Nolan Arenado leads to a trade, it's going to make the defense noticeably worse and hurt Marquez and all other Rockies pitchers' outlooks for 2020


Aaron Nola (SP, PHI)

One run behind Marquez and Lynn on the leaderboard are two interesting names: Aaron Nola and Sonny Gray. Nola is a groundball-heavy pitcher whose GB% has hovered around 50% in each of his Major League seasons. Last year the main difference was that his Zone% dropped 5% and his F-Strike% dropped to 7%, which meant he fell behind in the count often and allowed hitters to see more favorable situations and better pitches, causing a .045 point jump in BABIP and an almost 17% rise in Hard Contact.

If Nola regains his control and regresses more towards his career averages then he might see even more benefit from his defense this year now that he's trading in Maikel Franco (-.1 UZR and 133 OAA) for Scott Kingery (4.5 UZR and 74 OAA). I'd be inclined to buy into Nola, not shy away.


Sonny Gray (SP, CIN)

I have a little more pause regarding Sonny Gray's presence on this list. The right-hander experienced a resurgence last year due to a change in pitch mix. He abandoned his cutter altogether, which is good since it had a negative pVAL, and relied more on fastballs and a slider that saw a 10.4 jump in pVAL. His SwStr% rose 1.2% but his contact metrics stayed relatively similar, including allowing slightly more hard contact and less soft contact.

The changes in pitch mix were nice to see, but it also seems like he benefited from having OAA's ninth-ranked overall fielder Jose Iglesias (5.9 UZR) start at shortstop behind him. While Freddy Galvis isn't a major drop-off, his career UZR suggests that he doesn't match Iglesias' dependability, and Gray will also have Mike Moustakas play second base once Eugenio Suarez comes back. Moustakas has only played one season at the pivot and registered a -.1 UZR. Some of Gray's pitch mix changes signal real evolution, but the worse defense behind him is slightly concerning for a pitcher who saw his BABIP fall from .326 in 2018 to .255 in 2019. I'd expect some regression there, which likely means an ERA closer to last year's xFIP of 3.65.

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Using Sabermetrics for Fantasy Baseball: FIP and xFIP

The first advanced pitching stat most fantasy owners encounter is FIP. FIP stands for Fielding Independent Pitching, and attempts to measure a pitcher's actual skill instead of the effects of luck or his supporting cast. According to the DIPS theory, pitchers control only Ks, BBs, and home runs allowed. Therefore, Ks, walks, and dingers are the only inputs to determine the number.

For fantasy purposes, it is sufficient to understand the three primary inputs listed above and the fact that the stat is on the ERA scale. That means that if a FIP would be a good ERA, it is a strong FIP. The math is perfect, meaning that the league average FIP and ERA are identical (4.51 in 2019).

Sometimes xFIP is cited instead of FIP. The "x" stands for expected, and the stat is rooted in the fact that HR/FB varies for pitchers just as much as hitters. While FIP uses a player's actual homers allowed, xFIP charges him with a league-average amount of homers based on his fly balls allowed. Some pitchers are consistently more or less homer-prone than average, but studies show xFIP is a more reliable predictor of future ERA than regular FIP.


How to Use FIP and xFIP

This predictive nature of FIP and xFIP is the reason fantasy owners should care about them. Both metrics predict future ERA more reliably than ERA itself, making them a good go-to stat to determine if an early breakout may be for real or if a struggling superstar is likely to rebound. All things being equal, it is generally expected that a pitcher's ERA will regress towards his current FIP and xFIP over the long season.

For example, Marcus Stroman began the 2019 season on fire, posting a 1.43 ERA in March and April. However, his 3.90 xFIP suggested that he was nowhere near as good as he looked. Sure enough, his ERA ballooned to 4.31 in May (4.55 xFIP) and continued to fluctuate wildly throughout the year, including both highs (1.80 ERA in July) and lows (4.91 in August). If you started counting on him based on his hot start in April, you likely ended up disappointed.

There are certain types of pitchers that may consistently defy FIP. The first is knuckleball guys, who have challenged DIPS theory since its introduction. Sadly, nobody really threw a knuckleball in 2019, nor are any expected to make a fantasy impact in 2020.

The other type is simply called a "FIP-beater" that manages to control the quality of contact against him to the point that he outperforms his peripheral stats. Johnny Cueto has been a poster boy for this group for a while. He posted a sterling 2.25 ERA in 2014 before following it up with a solid 3.44 mark the next year. The 2016 season saw Cueto return to ace status with an ERA of 2.79.

Sabermetricians never saw Cueto that highly, however. His 3.30 FIP and 3.21 xFIP in 2014 made that campaign's 2.25 ERA look like a fluke, while his regression in 2015 (3.44 ERA, but 3.53 FIP and 3.78 xFIP) seemed like a harbinger of things to come. His sterling ERA in 2016 (2.79 ERA) was again undermined by considerably larger FIP (2.96) and xFIP (3.42) marks. Many analysts projected his demise in each of these years only to be proven wrong.

In 2017, they were proven correct. Cueto struggled to a 4.52 ERA, with a FIP (4.49) and xFIP (4.45) to match. His ERA rebounded to 3.23 in 2018 in an injury-shortened campaign (53 IP), but his underlying metrics (4.37 FIP, 4.67 xFIP) suggested that he was actually as bad as the previous year. Injuries limited him to 16 ineffective innings last season, leaving him as a wild card for 2020.

Pitchers like this rarely make good fantasy investments. Strikeouts are a key component of FIP, so pitchers who defy it are still lacking in a common fantasy category. Why risk a poor ERA for two-category upside? There is an ongoing debate in the sabermetric community though, so my word is not gospel on the subject.


What is SIERA?

SIERA stands for Skill-Interactive ERA and attempts to measure a pitcher's true talent more accurately than FIP and xFIP. It is marginally more predictive than xFIP, but its increased complexity may not be worth it. The stat assumes that ground ball pitchers will have a lower BABIP on grounders than other pitchers, while fly ball pitchers will have lower HR/FB marks. It is also adjusted for overall run-scoring environment and a pitcher's home park.

That may sound good, but remember that those adjustments won't affect your fantasy team's bottom line. German Marquez posted a 4.76 ERA for the Rockies last year, but his SIERA was only 3.85 in part to "correct" for Coors Field. Obviously, pitching at Coors will not improve your fantasy team's ERA. SIERA is also not on the ERA scale, with a league average of 4.41 to the 4.51 the other metrics last season.



To conclude, FIP and xFIP are metrics that try to determine the ERA a given pitcher deserves based only on the outcomes he actually controls: Ks, BBs, and home runs allowed. While FIP uses the pitcher's actual homers allowed, xFIP regresses it to the league average figure. Both metrics are on the ERA scale, and may be used to predict future ERA with more accuracy than ERA alone. Check out some of these articles if you want to learn more about applying sabermetrics within a fantasy context.

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Using Sabermetrics for Fantasy Baseball: Lineup Spot

You may be wondering why there aren't any advanced stats aimed at predicting a player's counting stats like runs and RBI. The answer is simple: modern sabermetrics reject the idea of a "clutch RBI guy" and therefore do not bother inventing predictive metrics for it. Runs and RBI are team-dependent stats and are unhelpful in ascertaining a given player's real value.

That might work for statheads, but fantasy owners frequently see 40% or more of a player's value tied to his RBI and run totals. We have to care about them. Drafting hitters from strong offenses can help pad the totals, but as you'll see, an even bigger advantage can be found by looking at a player's slot in the batting order.

Don't believe it? Yankees second baseman Gleyber Torres smacked 38 HR and hit .344 with runners in scoring position last season in a loaded lineup. Unfortunately for his fantasy owners, he hit fifth or lower for the Yanks in 93 of his 144 games played. His counting stats (96 runs, 90 RBI) accordingly fell well short of what you would expect given his other numbers. Here is a closer look at how to analyze a batter's lineup slot for fantasy purposes.


Lineup Slot & Counting Stats

In the table below, each batting order slot's PA, Runs, and RBI are presented from the 2019 season. The final number is simply R + RBI, an approximate measure of that slot's overall value to a fantasy team.

1st 22,823 3,323 2,320 5,623
2nd 22,281 3,279 2,623 5,902
3rd 21,760 2,917 3,021 5,938
4th 21,259 2,827 3,109 5,936
5th 20,794 2,627 2,848 5,475
6th 20,249 2,345 2,500 4,845
7th 19,705 2,195 2,267 4,392
8th 19,122 2,105 2,086 4,191
9th 18,518 1,849 1,699 3,548

Each batting order slot loses around 500 PAs compared to the slot before it. If we divide this total by the 30 current MLB clubs, we get a difference of around 17 PAs between consecutive hitters on one team. That may seem insignificant, but it compounds. For example, there is an average of 34 PAs separating a team's leadoff man from the three-hitter. Counting stats like Runs and RBI require an opportunity to accumulate, and hitters earlier in the batting order have more opportunity. Bear this in mind when comparing similarly skilled players on draft day.

RBI are highest from the cleanup spot, and trend downward in both directions from there. Leadoff hitters only get more RBI than the seventh, eighth, and ninth spots despite the largest PA total. This is because they never have runners on base before their first PA of the game, and need to rely on the weaker eighth and ninth hitters to get on in front of them after that. Since good hitters are usually clustered early in the order to maximize their PAs, leadoff men get minimal help from their teammates in producing RBI.

Runs peak at the leadoff slot and decrease from there. This decrease is not linear, as only 44 runs separate the first and second spots while 362 separate second and third. For this reason, fantasy owners want to stick to the early batting order slots where teams cluster their best hitters if possible. Leadoff guys have the most opportunity and the team's best hitters hitting behind them, so they score a lot of runs for the same reason they do not register many RBI.

Finally, the R+RBI column refutes the idea that a team's heart of the order is 3-4-5. It is actually 2-3-4, the only lineup slots to approach 6,000 combined R+RBI. The 1st slot is great for runs scored, and the 5th spot offers a respectable 5,475 R+RBI. However, the others clearly lag behind. This means that a player in the middle of a weaker offense is likely to outproduce a player on the periphery of a stronger one. Platoons, injuries, and lineup shuffling can change these numbers, but in general the earlier the slot, the better for fantasy purposes.



To conclude, counting stat production depends on opportunity and team support. Players that bat early in the order tend to get more of both, though leadoff men give up RBI potential for increased runs scored. If you would like to learn more about how to apply sabermetrics within a fantasy context, check out some of the other articles linked here.

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Using Sabermetrics for Fantasy Baseball: Pull%

We have previously determined that fantasy owners generally prefer batters to hit the ball into the air in order to have a chance at a home run. Yet, all fly balls are not equal for this purpose. A player can maximize his power production by pulling the ball in the air.

One way to illustrate this is to look at league-wide HR/FB by batted ball direction. Flies to the opposite field seldom found the cheap seats, posting a HR/FB of just 6.1% last season. Flies to dead center fared slightly better (10.8% HR/FB), but pulled fly balls were clearly the most productive (37.1% HR/FB).

Let's take a closer look at how Pull% can help you win your fantasy leagues in 2020!


How to Interpret Pull%

In 2019, roughly 58% of all home runs were to the batter's pull side. Only 16% of homers went to the opposite field, with the remaining 26% going out to center. This distribution is fairly consistent year-to-year, so it's safe to count on something similar going forward.

In a way, this makes intuitive sense. Pulled baseballs tend to be hit with the highest exit velocity, making it easier for them to leave the stadium. The power alleys next to the foul poles on either side of the ballpark also present the shortest distance to the cheap seats. If a player's HR/FB dramatically improves, a change in approach involving more pulled baseballs could help explain why.


How Pull% Affects Fantasy Performance

Boston's Xander Bogaerts provides a good illustration of this kind of change. In 2015, he pulled only 16.7% of his fly balls, producing a HR/FB of 5.3% and a total of seven dingers. He significantly upped his power game in 2016 by pulling 28.1% of his flies, leading to a much higher 11.4% HR/FB and 21 bombs on the campaign. The increased power was not exclusively the result of the Pull% spike, as he upped his FB% as well (25.8% in 2015, 34.9% in 2016). It helped to validate his HR/FB increase, though.

His change in approach did not last. Bogaerts pulled only 24.5% of his flies in 2017, dropping his HR/FB to 7.2% and his season HR total to 10 in the process. Once again, the raw number of fly balls Bogaerts hit decreased (30.5% FB%), so the change in Pull% was not solely responsible for the loss of power. This example illustrates that while a change in Pull% can support an increased HR/FB, it will only last so long.

In 2018, Bogaerts clubbed 23 HR on the back of a FB% spike (35.6% FB%) and a 15.5% HR/FB. However, his Pull% on fly balls decreased to 23.7% that season. Bogaerts finally put it all together last season, posting a career-best 33 long balls on the back of a career-best FB% (39.8%) and a 29.3 Pull% on those fly balls that contributed to a 16.7% HR/FB.


The Problem with Raw Pull%

Of all pulled baseballs in 2019, 58.2% were grounders. Pulled grounders might have a higher average exit velocity than other ground balls, but the shift still eats them up with minimal difficulty. They will never turn into home runs. In contrast, only 21% of pulled baseballs were classified as fly balls last season. Ideally, fantasy owners want their hitters to pull fly balls while limiting how often they roll grounders to their pull side.

This is much easier said than done, as all players pull many more grounders than flies. Let's consider Mike Trout as an example. His raw Pull% of 42.4% was marginally higher than the league average 40.7%, and he pulled 61.6% of his grounders compared to 28.7% of his flies. At first glance, you might think that Trout was making himself vulnerable to the shift without significantly boosting his power potential.

That assumption would be wrong. The shift was designed for batters who pull much more than 61.6% of their ground balls, allowing Trout to hit a solid .303 against it last year. Many batters fail to pull even 20 percent of their flies, so Trout rated well enough in that regard as well. Pulling more grounders than flies is far from a death sentence.



To sum up, pulled fly balls tend to perform better than other fly balls. This means that pulling more flies can produce an increased HR/FB, but you should never use raw Pull% to determine this. Most pulled balls are hit on the ground, where all of the exit velocity in the world cannot turn them into home runs.

Therefore, you should filter a player's Pull% by batted ball type to produce the most reliable results. If you're interested in learning more about the role of advanced analytics in today's fantasy environment, check out some of our other articles here!

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Using Sabermetrics for Fantasy Baseball: Plate Discipline

No matter how high a particular player's BABIP may be, his average will be mediocre at best if he strikes out too much. This is why fantasy owners have known for years that players like Chris Davis are potential drains on a fantasy team's batting average. Furthermore, players that whiff a lot tend to continue to do so - it is a very sticky trait.

In 2019, the league average K% was 23%, meaning that roughly one in five MLB PAs ended in a K. Players who strikeout less frequently tend to hit for higher averages, while more strikeouts hurt batting average. Of course, a player may put up a fluke K% just as easily as a fluke BABIP.

Let's learn how analyzing stats related to plate discipline can help improve the performance of your fantasy baseball team entering the 2020 season.


How to Interpret Plate Discipline

Sabermetrics may be used to determine whether a given player "deserved" his K% over a particular period. The first number to check is SwStr%. This metric simply tracks what percentage of pitches a batter swings and misses at. The league average was 11.2% in 2019, with higher numbers indicating a proneness to K. If a player improves his strikeout rate without a corresponding improvement in SwStr%, the improvement is unlikely to stick moving forward. Likewise, a career-worst strikeout rate backed by a normal SwStr% is likely to regress in the player's favor.

Notably, Baseball Savant's Whiff% is not the same thing as SwStr%. Whiff% measures how often a batter swings and misses on all swings, while SwStr% uses all pitches seen instead. Whiff% figures are therefore much higher than SwStr%. Since SwStr% is used much more frequently in fantasy analysis, the rest of this article will use it.

Further detail is offered by O-Swing%, a measure of how often a batter swings at a pitch outside of the strike zone. Batters usually want to hit "their pitch," which they never get to see if they pop-up a fastball over their head early in the count. In 2019, the league averaged an O-Swing% of 31.6%. Numbers significantly higher than this indicate an increased likelihood of chasing a bad pitch and making poor contact or striking out.

This stat is also used to examine a player's walk rate, or BB%, in much the same manner as SwStr% is used to double-check K%. A strong walk rate when a player is still chasing too many pitches is not based in any repeatable skill, and will likely be normalized moving forward. Likewise, a lower walk rate paired with a career average O-Swing% indicates that the walks should come back.

Fantasy owners should always care about walks even if their format does not directly reward them. Every BB is a chance to steal a base or score a run, and players that know the zone tend to hit for higher averages to boot!


Evaluating Players Through Plate Discipline

Joey Votto is widely regarded as the master of plate discipline, and his surface stats support the assessment. His 12.5 BB% was really strong despite a down year overall, while his 20.2 K% bested the league average. Digging deeper, we find that these numbers are completely justifiable. His 21.1 O-Swing% was more than 10 percentage points better than the league average rate, and his 7.3 SwStr% was below the average as well. It is safe to conclude that Votto will continue to demonstrate outstanding plate discipline in 2020.

Cincinnati's Aristides Aquino does not measure up as well. He hit a reasonable .259 last season in spite of striking out at a 26.7% clip. He chased too many pitches outside of the strike zone (41.2%), making it tough to project a repeat of his 7.1 BB%. Worst of all, he whiffed at 18.9% of the pitches he swung at, one of the worst marks in baseball. Owners are drafting Aquino for elite power upside, but his plate approach is so bad that his batting average and OBP could crater to unrosterable levels.

Aggression or passivity at the plate can confound this analysis slightly. For example, Aquino was extremely aggressive last year with a Swing% of 55.7%. The league average was 47% in 2019. Even if a hitter has a high chase rate, he can't strike out if he resolves the PA before three pitches are thrown. Votto is on the opposite side of the spectrum, as his refusal to swing at borderline pitches (41.5 Swing% last year) leads to more Ks than his raw SwStr% numbers would suggest.

Other plate discipline metrics exist, such as Z-Swing%, O-Contact% and Z-Contact%, but SwStr% is usually a good enough proxy for fantasy purposes. One exception to this rule is a change in SwStr% rooted exclusively in pitches outside of the zone. Sometimes, missing those pitches can be better than hitting them.



To conclude, both K% and BB% are useful for fantasy purposes but fail to tell the whole story. SwStr%, or how often a batter swings and misses, is a better indicator of a player's future strikeout rate than K% alone. O-Swing%, or how often a batter chases pitches outside of the zone, performs similarly concerning BB%. If you would like to learn how to use more metrics to determine fantasy performance, check out our other articles on the subject here!

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Using Sabermetrics for Fantasy Baseball: Batted Ball Distribution (Hitters)

Fly balls can turn into home runs. Ground balls never do. It would seem as though fantasy owners want their batters to hit nothing but flies, yet this is not always the case.

Why would this be? The answer, of course, comes down to batted ball distribution and the manner in which batters make contact. The league average batted ball distribution in 2019 was 21.4% line drives, 42.9% ground balls, and 35.7% fly balls. Most individual players vary from this standard breakdown, providing insight into their fantasy viability.

In this article, we'll continue evaluating the most effective ways to use sabermetrics to get an edge in your fantasy baseball leagues.


The Value of Line Drives

Let's first look at how all major leaguers fared on each of the major types of batted ball in 2019. Grounders generated a BABIP of .236. Flies were not as productive, posting a .118 figure. This makes sense, as pop-ups almost never fall in, cans of corns to the outfield are only slightly better, and homers are considered out of play and do not count toward BABIP. Line drives turned into base hits far more frequently than either of the others, posting a .678 BABIP. The difference between liners and anything else is startling. Batters want line drives.

Tim Anderson's stellar 2019 provides a good illustration of what a few extra liners can do. He posted a .335/.357/.508 triple-slash line thanks in part to a 23.8% line drive rate, a far cry from his career line of .276/.303/.475. Anderson was an afterthought in real and fantasy baseball terms heading into 2019 but ended up surprisingly productive in both areas.

A player's LD% tends to bounce around the league average with random spikes and drops, none of which offer much predictive value moving forward. Anderson has a 20.9 LD% over his career, so luck was almost certainly the primary driver of his 2019. When BABIP is driven by luck, LD% is usually why.

This is not to suggest that no one consistently posts above-average LD% rates. For example, Joey Votto's career .349 BABIP is driven by his career 25.7 LD%. Considering the length of his career, it would be stupid to suggest that Votto has enjoyed a lucky decade-plus. Therefore, we give credit to Votto for being a plus-BABIP guy due to a LD% skill, just like we give Christian Yelich BABIP credit for his blinding speed. This distinction has to be earned over numerous full seasons, however. Most LD% surges are more fluky Tim Anderson than sustainable Joey Votto.


Which Is Better: Ground Balls or Fly Balls?

Unlike LD%, both GB% and FB% are stickier--a player with an elevated rate in one is likely to repeat a similar rate moving forward. By BABIP alone, grounders are better. However, this changes significantly if slugging percentage is considered. In 2019, grounders offered a slugging percentage of .258, only slightly higher than the .236 BABIP they posted. Flies had a .788 slugging percentage, easily offsetting the lower BABIP for most fantasy players. Sluggers like Cody Bellinger who lift the ball at an exceptional rate have a built-in slugging advantage because of their batted ball profiles.

The ideal batted ball mix, therefore, varies with the player. Elite speedsters like Billy Hamilton want more grounders than flies, as his career 3% HR/FB is never producing a lot of homers anyway. Sluggers like Albert Pujols want fly balls, especially since the shift and his lack of speed prevent him from realizing the larger BABIPs associated with grounders anyway. Fantasy owners usually prefer players with power and speed potential to have a higher FB%, as the extra power is more beneficial than a few extra times on base.

Incidentally, line drives averaged a ridiculous .916 slugging percentage to go with their .678 BABIP in 2019, so they are still the batted ball of choice.



To conclude, line drives are by far the most productive result for hitters. BABIP's luck-driven fluctuations are driven by LD%, a largely random stat. GB% and FB% are more predictive, and which one is favored depends on the hitter in question. Grounders offer a higher BABIP, but almost zero power. Flies result in base hits less often, but generate much more power when they do. The intricacies of BABIP could be a never-ending topic, but the information provided so far is generally enough for fantasy purposes. Check out this link to learn about some other metrics that can help you predict a player's performance.

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K-BB% Risers: Five Undervalued Draft Targets

Before I dive into the purpose of this article, I would first like to introduce myself. My name is Michael Simione, also known as @SPStreamer, and this is my first article over here at RotoBaller. For those who don’t know me, my favorite aspect of baseball is, without a doubt, pitching. Most of my articles will be about pitchers and if you have any questions please feel free to reach out. I feel very lucky to be a part of the RotoBaller team and am extremely excited to see what the future holds!

K-BB% is one of the best metrics to use during the season and to measure a pitcher's performance. Simply put, the best pitchers have a high K-BB% because they strike out batters at a high rate and walk them at a low rate. In order to understand K-BB%, you have to understand K% and BB%. Luckily, they are very easy to calculate as you just divide the pitcher's strikeouts or walks by the total number of plate appearances. The way you get K-BB% is by subtracting K% from BB% and you get your K-BB% total. In this article, we will look at five pitchers who have increased their K-BB%. The table below shows each pitcher's K%, BB%, and K-BB% dating back to the second half of 2018.


Yu Darvish, Chicago Cubs

Yu Darvish had one of the biggest mid-season turn arounds in recent memory. In the first half of 2019, Darvish struggled with a 5.01 ERA, 5.31 FIP, and 14.8 K-BB%. But when the calendar turned to July he became a completely different pitcher, posting a 2.76 ERA, 2.83 FIP, and 35.6 K-BB%. There were a couple of factors as to how Darvish was able to do this and why it might be sustainable.

In the second half, Darvish had better command and control of his pitches. The first sign was his decrease in BB% as it went down from 11.70% to a whopping 2.20% (lowest of any pitcher in the second half). He did this by shortening his extension on his delivery, Darvish was trying to extend too far and it was resulting in a loss of control. For instance, when he made this mechanical change, his fastball’s ISO went from a .434 to .204 which shows a massive shift in control.

Darvish messed with his pitch mix quite a lot as he essentially throws five different pitches (four-seam fastball, sinker, changeup, slider, curve, cutter, and splitter), but he mainly changed the usage of his four-seam fastball and cutter. By lowering his fastball usage and upping his cutter usage, it resulted in Darvish having an overall lower wOBA (.320 vs .261) and Barrel% (8.6 vs 5.9).

With these changes, Yu Darvish finally became the pitcher we had all had hoped for. The main key to his success is his walk rate, as it has always been his Achilles heel (career 8.8 BB%). If Darvish keeps these changes heading into this season he could very well have a great 2020 campaign. With his current ADP of 66, there is plenty of room for value.


Lucas Giolito, Chicago White Sox

Lucas Giolito finally had the breakout season we were waiting for by pitching his way to a 3.41 ERA, 3.43 FIP, and 3.57 SIERA in 2019. He had one of the most rapid ascents of K-BB% from the second half of 2018 to the end of 2019. In the three halves of baseball, his K-BB% went from 10.30% to 20.60% and then to 29.10%. In the matter of a year and a half of baseball, it went up 18.80 percent! What is also impressive is his SwStr% also went from 9.6% in the second half of 2018 to 15.6% in the second half of 2019.

Giolito’s success stemmed from perseverance and velocity. The White Sox weren’t doing Giolito any favors, so he decided to get outside help, which was the best decision he ever made. He met up with his former high school pitching coach who changed his delivery and made it more efficient. Overall in 2018, his average fastball velocity sat at 92.8 MPH, but in 2019 it sat at an average of 94.6 MPH. The increased velocity completely morphed his fastball, as his walk rate went down and the pVAL went from -13.5 in 2018 to 20.5 in 2019.

With a now insane 12 MPH difference between his fastball and changeup, a domino effect happened as his changeup became a better pitch in 2019. Compared to 2018, his changeup had a better O-Swing%, SwStr%, BAA, and wRC+ against.

Giolito’s newfound fastball lead him to become one of the most improved starters in baseball by increasing his K% and lowering his BB%. It’s the heart and soul behind the dramatic turnaround from a 6.13 ERA in 2018 to a 3.41 ERA in 2019. Giolito’s current APD of 48 is right on par with where it should be and you should feel confident taking him as an SP2.


Luke Weaver, Arizona Diamondbacks

Luke Weaver was a popular bounce-back candidate in 2019 after his 2018 season let down (4.94 ERA and 4.20 K-BB%). After 12 starts in 2019, it seemed as if Luke Weaver was in full break out mode as he was dominating hitters leading him to a 2.94 ERA, 3.07 FIP, and a 21.30 K-BB%. Unfortunately, an injury to Luke Weaver’s pitching arm shortened his season.

The main force behind Weaver’s success was his change in pitch mix. In the second half of 2018, he featured a four-seam fastball, changeup, and curveball. The four-seam fastball and changeup were serviceable pitches but Weaver would pound the strike zone with his curveball and it posted terrible results. It let up a .294 batting average against with a .265 ISO, which means he had control but no command and hitters jumped all over it.

In the time he pitched in 2019 he decided to add a cutter to his pitch mix and lower his curveball and four-seam fastball usage. The cutter essentially replaced his curveball as his go-to pitch when it came to needing a strike, and it worked well. In 2019 the cutter posted a .263 batting average against with a .158 ISO. Adding this pitch was huge as it also improved his four-seam fastball, curveball, and changeup.

Between the second half of 2018 and his 2019 season, Weaver saw a dramatic rise in his K% and a slight drop in his BB%. When you see a dramatic difference in players K-BB% like this it always means good things are happening, especially when it was due to him adding a fourth pitch. His current ADP is at 194 and while some might be worried about his arm injury his price makes him well worth the buy.


Robbie Ray, Arizona Diamondbacks

Robbie Ray is one of the most fascinating pitchers in baseball. He has some of the best pitches in the game but when it comes to command and control he tends to struggle. Ray is one of the best strikeout pitchers in baseball as he had a K% of 31.4 in 2018 and 31.5 in 2019. To go with it, he also had an impressive SwStr% of 12.8 and 13.6. Unfortunately with those strikeout rates came a horrendous 13.3 and 11.2 BB%.

In 2019, Robbie Ray’s best month of baseball came in July, where he posted a 3.26 ERA and 28.0 K-BB%. That month was his lowest BB% in any month of the three halves we see in the table above. He noticeably upped his four-seam fastball usage to 50% which lowered his walk percentage to 7.2%. He seems to have the most control with his fastball as he pounded the zone 57.3% of the time with it in 2019. This might be the key to Ray dropping his walk percentage.

If Ray can continue to drop his walk rate and push his K-BB% up to around 28% he can become an elite pitcher in baseball. He maybe on his way because in the three halves we looked at above, it has dropped from 13.80% to 12.10% to 10.00%. Robbie Ray is currently going at pick 151 and he holds value at that spot, especially if you are looking for strikeouts.


Tyler Beede, San Francisco Giants

Tyler Beede is an interesting late-round pick in 2019. He has popped up recently because he is one of a handful of pitchers that had three pitches with a SwStr rate of 15.0% or higher (curveball, changeup, slider/cutter). This makes Beede very intriguing and even more so intriguing because of his increase in K-BB% between the first and second half of last year (7.30% to 16.40%).

Halfway through the season, Beede decided to add another pitch to his repertoire which, depending on the site, is classified as both a slider and a cutter. If you watch him throw this pitch, the confusion is understandable because at times it breaks diagonally like a slider while at times it has a late break across like a cutter. This pitch was monumental in Beede’s development as it hits all of the marks with a 35.8 O-Swing%, 38.0 Zone%, and 17.6 SwStr%. If that wasn’t enough to get you excited it also produced a .273 average against with a .345 BABIP, which means you can expect it to be even better next year.

With Beede’s new pitch, it’s no wonder he is becoming a popular sleeper pick for 2020. His current ADP of 373 will cost you nothing in drafts. Taking him as a late-round flier could benefit you greatly in the long run and be one of the reasons you win your fantasy league in 2020.



The best pitchers in baseball always have a high K-BB% and it is very important to scout out the pitchers that are creating more strikeouts while giving up fewer walks. While looking at K-BB% splits is important, make sure to do your due diligence and dive into a pitcher to see if there is a reasoning behind it. Look for velocity change, pitch mix change, or even a mechanical change. Thank you for reading and as always, feel free to reach out to me with any questions!

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Using Sabermetrics for Fantasy Baseball: Barrels

Last time, we looked at how exit velocity (or EV) is only one piece of the fantasy analysis puzzle. Baseball broadcasts will commonly cite Launch Angle (LA) to complement their EV figures, but it is given in terms of degrees. Am I evaluating a baseball player or trying to find the hypotenuse of an isosceles triangle? Let's simplify things a bit to see how these numbers can actually benefit our analysis.

LA is basically a fancy way of saying things that the fantasy community has used for years. They don't do a good job of publicizing it, but LA is actually fairly simple to understand. Here is the batted ball type produced by the various degree measurements:

Batted Ball Type Launch Angle
Ground ball Less than 10 degrees
Line drive 10-25 degrees
Fly ball 25-50 degrees
Pop-up More than 50 degrees


What is a Barrel?

Most batters want to live in the 10-50 degree range, as grounders rarely produce power while pop-ups rarely produce anything other than easy outs. Well-struck balls in this range of launch angles are the batted balls that fantasy owners are most interested in. A Statcast stat called "Barrels" filters out everything else, allowing us to evaluate who is hitting most of these high-value batted balls.

A Barrel is defined as "a ball with a combination of exit velocity and launch angle that averages at least a .500 batting average and 1.500 slugging percentage." It should be noted that the numbers above are only a minimum threshold. In this respect, the stat is like a Quality Start. It is possible to register a QS with an ERA of 4.50, but the actual average ERA of all MLB Quality Starts falls well below 4.50.

The range of EVs and LAs that combine to form Barrels is called the Barrel Zone. This means that higher EVs can compensate for less ideal LAs to produce the .500/1.500 minimum. Batted balls must have an EV of at least 98 mph and fall within the 10-50 degree LA range in order to be classified as Barrels. We care about fantasy production, not the intricacies of a mathematical relationship. You don't need to worry about the math.

With this in mind, Jorge Soler led baseball in Barrels last year with 70. He was followed by a three-way tie at 66 (Ronald Acuna Jr., Pete Alonso, Mike Trout) and Nelson Cruz (65). This group passes the sniff test, as it includes the HR leader in each league and the best player in the game. Likewise, Billy Hamilton managed zero Barrels all year, living up to his reputation of weak contact. Still, we already knew this. What do Barrels add to the equation?


The Value of Barrels

They become more instructive when you stop looking at them as a counting stat and start examining them as a rate stat. By taking the number of Barrels and dividing by the total number of Batted Ball Events (BBE), we get a percentage that tells us how frequently a player's batted balls are Barrels.

Joey Gallo topped this list in 2019 with a 26.4% Brls/BBE figure, followed by Miguel Sano (21.2%), Aaron Judge (20.25%), Nelson Cruz (19.9%), and Gary Sanchez (19.1%). Guys like Sano and Judge didn't have the raw BBEs to crack the Barrels leaderboard, but the rate stat suggests that they could be intriguing values this year.

This data helped identify sleepers in every year of its existence. Chris Carter had an 18.7% Brls/BBE in limited 2015 playing time. He led the NL in homers the next year with 41, so he was a sleeper worth owning based on the prior year's Brls/BBE. Gary Sanchez ranked eighth in the league with a 15.8% Brls/BBE in 2016, foreshadowing his ascension to the top of the catcher rankings after a strong 2017. Gallo's 22.1% rate of Brls/BBE over 253 batted balls in 2017 suggested that his 41 HR were real, and he effectively repeated them the next season (40 HR). Likewise, Luke Voit's third-place finish in Brls/BBE in 2018 foreshadowed his .263/.378/.464 line with 21 HR in 510 PAs for the Yankees last year.

Like BABIP, Brls/BBE also seems prone to random fluctuation. Giancarlo Stanton's amazing 2015 (he hit 27 bombs in 318 PAs) was fueled by a 32.5 percent Brls/BBE, over 10 points higher than the league's second-best performance that year (Miguel Sano's 22.4% rate in limited time). A rate that high was almost certainly an outlier. Sure enough, he regressed to a still strong 17.3% Brls/BBE in 2016, 17.4% rate in 2017, and 15.1% in 2018 before missing most of last year due to injury.



Viewing Barrels as a rate stat can be beneficial, but important considerations like strikeout rate still aren't captured by the metric. That said, few metrics have proven to have the predictive power that Brls/BBE has shown in recent years. There are a few misses (Tyler O'Neill led baseball in Brls/BBE in 2018 but did nothing useful last year), but in general it's a stat you want to look at. Here are some other stats you can look at to become a more effective fantasy owner.

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Using Sabermetrics for Fantasy Baseball: BABIP for Hitters

The most accessible of the fantasy-relevant advanced stats is BABIP, or Batting Average on Balls In Play. It simply measures a player's batting average on balls in play, with outcomes such as strikeouts and home runs removed from consideration. In general, the league average hovers around .300, a nice round number to remember.

Many know BABIP as an approximation of luck, with either a very high or very low number indicative of a major batting average regression in the future. That is partially correct--the stat can be used to predict batting average fluctuations. However, a player's skills may allow him to run a better than average BABIP, or doom him to a consistently below-average figure. One example of this is Christian Yelich.

Let's see how this metric can be used to evaluate one of the most impactful bats in the majors and how you as a fantasy owner should use it to your advantage when preparing for your upcoming drafts.


The Above-Average BABIP Formula

Yelich has been a batting average force for a while now, but the addition of elite power to his profile has made him a consensus top-3 pick heading into 2020. In 2019, Yelich hit an outstanding .329/.429/.671 with 44 HR and 30 SB in just 580 PAs. One of the reasons for his success was a .355 BABIP, so Yelich loses a lot of value if we regress that all the way to .300. Should we really do that?

Yelich's career BABIP is .358, clearly indicating a sustainable ability to beat the league average .300. Considering last year's BABIP of .355 was within three points of his career rate, a repeat is the safest projection. What skills does Yelich possess that allow him to crush the average player?

Yelich is an elite speedster--his 28.7 ft./sec Statcast Sprint Speed was nearly two full ticks above average last year. It makes sense that someone with Yelich's wheels could beat out more base hits than other players, while most catchers would lag in this regard. Therefore, an established player's baseline BABIP should not be the league average .300, but whatever that specific player's career BABIP is.

Looking at BABIP by batted ball type can also be a great tool. Yelich gets his speedster hits exclusively on grounders, as running really fast does nothing to prevent a fielder from catching a ball in the air. While the league averaged a .236 BABIP on grounders, Yelich posted a .292 mark on them last year. His career rate is only .276. Therefore, we can conclude that Yelich will continue to outperform the league average on ground balls because his .276 career BABIP is much higher than the league average. However, he is unlikely to do so to the same extent he did in 2019.

Comparing BABIPs for the other batted-ball types year over year is something of a mixed bag for Yelich. His fly balls found pay dirt much less frequently, posting a BABIP of .133 against a career mark of .201. However, his line drives fared considerably better (.744 BABIP last year) than they have in the past (.690 career). Overall, both figures should be expected to regress to the mean and roughly cancel each other out. When we factor in slight regression based on ground balls above, we should probably expect Yelich to fall just shy of last year's BABIP while still clearing .300 easily in 2020.


The Below-Average BABIP Formula

The same trend is possible in a negative way. For example, Anaheim's slugging DH, Albert Pujols, is well known for being an all-or-nothing batter that pulls the ball at every opportunity. This makes him susceptible to the shift, as the infield defense knows where the ball is likely to go and can set up accordingly. He also lacks the speed to beat out infield hits most other major leaguers can, finishing second to last in Statcast's Sprint Speed metric last year.

These factors figure to hurt his BABIP on grounders, and Pujols's .212 last year indicates that it did. This is not a new trend, as he hit .160 on grounders in 2018, .192 in 2017, .217 in 2016, and .179 in 2015. Clearly, projecting regression toward the league average would be wrong, as his pull tendencies and subpar speed allow the defense to consistently perform better than average against him. Pujols's overall BABIP was .238 last year, a number that should be expected moving forward due to his consistently poor production on ground balls.



To conclude, BABIP can be used to indirectly measure a player's batting average luck by comparing it not to the league average of .300 but to an established player's career number. Foot speed, batted ball authority, line drive rate, and defensive positioning all give players some ability to manipulate BABIP. Players with these skills may still overachieve, and this regression can be predicted by examining BABIP by batted ball type. Younger players without an established baseline are generally regressed to the league average, but these predictions are less reliable than those based on a player's personal history. Click on this link to learn more about how sabermetrics can give you an advantage as a fantasy baseball owner!

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Using Sabermetrics for Fantasy Baseball: Exit Velocity

If you've watched a baseball broadcast in the so-called Statcast Era, you have undoubtedly noticed the broadcasters commenting on a batted ball's exit velocity, or EV. Many have taken to using stats like Hard% and Soft% to forecast how a player should be performing, expecting larger Hard% rates to produce larger BABIP and HR/FB figures. There is a relationship there, but it is not as clear-cut as you might think.

The hardest batted ball of the 2019 season was struck by Giancarlo Stanton. It was clocked at 120.6 mph, but Stanton only recorded a single for his efforts. The hardest-hit home run was a three-way tie at 118.3 mph: Gary Sanchez, Peter Alonso, and Aristides Aquino. Aaron Judge's best EV of the season was clocked at 118.1 mph and made an out. While higher exit velocity figures support offensive performance, you need to use other tools as well to accurately assess a player's performance.

The best way to get a feel for how hard a given batter usually hits the ball is to look at his average exit velocity. The league average mark in 2019 was 88.1 mph, but that stat is of little value. The exit velocity on airborne balls (both flies and line drives but not including pop-ups) is all you need when evaluating a player's HR/FB rate, while ground ball exit velocity is the best indicator of a high BABIP on ground balls. The two metrics should almost never intersect, but a lot of analysts ignore context and use overall average exit velocity to evaluate both HR/FB and BABIP. You really shouldn't do that unless you believe that a grounder has a chance of going over the fence. Hard% is even worse, as it assumes that all batted balls of at least 95 mph are equal and makes no attempt to differentiate ground balls from airborne ones. So how do you figure out what's useful among these sabermetric measures? As always, the answer lies in placing these numbers in their proper context.


How can I use EV to predict BABIP on ground balls?

Major league batters averaged an EV of 84.8 mph on ground balls last season, and every mph above or below that figure is very important. For instance, hitters produced a batting average of just .150 on balls in the 80-82 mph bucket in 2019, while batted balls at 85 mph or above produced a .347 batting average.

As we've previously seen, players who can be shifted should be expected to struggle on grounders regardless of EV, while faster players can punch above their weight. Much like broader BABIP, it is a good idea to compare a player's current BABIP on ground balls to his career mark to account for these factors. As such, average exit velocity on grounders should be seen as one piece of a larger puzzle instead of the end of your BABIP analysis.


How can I use EV to predict HR/FB?

In 2019, the average airborne exit velocity in Major League Baseball was 92.7 mph. All other things being equal, a batter with an average airborne EV in the same area would be expected to be near the league-average HR/FB. Unfortunately, nobody of fantasy interest matched the exact MLB average last season.

If we increase the threshold to 92.8 mph, we get a bunch of fun names to work with: Gleyber Torres (21.5% HR/FB), Charlie Blackmon (17.7%), Jose Altuve (23.3%), and Jonathan Villar (16.7%). By exit velocity alone, all four of these guys are due for significant regression that could adversely affect their fantasy value. However, all four of these guys played in power-friendly ballparks last season, and three of them will do so again (sorry Villar). While you might want to expect some regression from Torres and Altuve, their parks will probably inflate their HR/FB to some degree moving forward.

Some of the other factors that can impact HR/FB include Pull% and Launch Angle, both of which will be discussed in greater detail later in this series. While airborne EV is an important power metric to look at, there are other variables that can prove more important. Ironically, airborne exit velocity's most important use may be to confirm whether a player besting his career BABIP on fly balls and/or line drives can continue to do so.



Hitting the ball hard is obviously a good thing, but limiting your fantasy analysis to just exit velocity is asking for trouble. Variables such as strong pull tendencies and foot speed can trump raw EV in a player's BABIP on ground balls, while home park, Launch Angle, and Pull% can all support elevated HR/FB figures even if the EV doesn't. Oh, and for the love of the fantasy baseball gods, please don't use Hard% for anything.

If you'd like to learn how to interpret more statistics for a fantasy advantage, please click on this link and check out our other articles on the topic.

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Using xwOBA to Identify Breakout Pitchers

Heading into a new baseball season, fantasy owners are looking for any leg up on our competition. We turn over stones looking for any stat or training report that might suggest a breakout that few others can see coming. I’m not here to promise some groundbreaking stat, but I do believe that Expected Weighted On-base Average (xwOBA) can be useful when looking for hints as to how a player might perform.

xwOBA is calculated using exit velocity, launch angle and, Sprint Speed to evaluate the quality of contact that a batter makes or a pitcher gives up. While, as Craig Edwards effectively points out, it is not a predictive stat, it “can help explain how a pitcher has arrived at his runs-allowed total.” More specifically, when comparing xwOBA with wOBA, we can start to see if a pitcher earned the batted ball results he gave up or if the results were due to factors outside of his control. Essentially, did a pitcher deserve better numbers than he wound up with.

The following article will look at some of the pitchers who underperformed their xwOBA. By using the simple equation of [wOBA – xwOBA], we can find pitchers who had an xwOBA that was lower than his wOBA, suggesting that he should have statistically performed better based on the quality of contact made against him.


What Do You Expect?

As a point of comparison, some of the best starting pitchers in terms of their xwOBA last year were Gerrit Cole (.238 xwOBA), Justin Verlander (.249) Jacob deGrom (.253), and Max Scherzer (.254). For relievers, some leaders last year were Emilio Pagan (.221 xwOBA), Kirby Yates (.224), and Liam Hendriks (.229).

Below is a table of pitchers who faced at least 100 batters and are intriguing based on how they underperformed their expected results. Some of them are high-end arms who have the potential for a better 2020 season, some are players who have strong statistical numbers that could benefit from a new role, and others are players who could we are simply rolling the dice on in hopes that they become more fantasy relevant.

Player wOBA xwOBA Difference
Mitch Keller .392 .314 .078
Edwin Diaz .344 .277 .067
Darwinzon Hernandez .327 .263 .064
Justus Sheffield .376 .321 .055
Elieser Hernandez .340 .290 .050
Josh James .304 .263 .041
Blake Snell .301 .264 .037
Lucas Sims .302 .271 .031
Matthew Boyd .320 .297 .023
Noah Syndergaard .301 .280 .021


Blake Snell, Tampa Bay Rays

Locked-in SP1

I don’t think there are many people who are shying away from Snell in fantasy leagues, but after his Cy Young Award-winning season, injuries derailed his 2019 follow-up. While his overall numbers may not have been in line with last year’s leading fantasy aces, Snell’s expected stats suggest that he underperformed his overall ability. In fact, his .264 xwOBA is just behind Scherzer and was the 6th best number of all pitchers with at least 350 batters faced (Snell only faced 441 due to injury). His xBA was third-best at .205, as was his xSLG at .327, a whopping .064 points lower than the actual slugging percentage that he gave up last year.

We mentioned that expected stats aren’t predictive, but they are reliable year-to-year, so Snell’s consistency between 2019 and 2018 (.203 xBA, .340xSLG, and .273 xwOBA) suggests that he pitched just as well, if not better, last year and we should see something similar in 2020. With the Rays still putting a strong offense behind him, Snell should remain a safe SP1 in fantasy drafts, and I would draft him over guys like Shane Bieber and Stephen Strasburg, who are currently going ahead of him.


Edwin Diaz, New York Mets

Top-Five closer

Edwin Diaz was always likely to be a volatile fantasy asset; however, nobody could have expected last year's meltdown. A 5.59 ERA with a 4.51 FIP and a 1.38 WHIP caused many fantasy owners, and Mets fans, to freak out. Despite his BB% rising and his SwStr% decreasing, his final numbers were still well within his career range and the range of most strong relievers. His 17.8 SwStr% was a drop of 1.1 from 2018, but would have put him 4th in MLB if he had enough innings to qualify, and his 8.7% walk rate was a 2.6% increase, but still well below his 2017 numbers.

For all intents and purposes, it seems like Diaz just had a bad and unlucky season. His home run per nine innings rate was 2.33, which was one of the worst rates among relief pitchers and a ridiculous jump from his 0.61 rate in 2018. He also added to that a BABIP of .377, which was the second-worst in the league. All of that, plus the .067 difference in xwOBA and wOBA tells me that even in this nadir, he still was pitching better than his results. I fully expect a bounce-back to a top-five closer who I'd gladly take over Liam Hendricks, Ken Giles, and Will Smith - all who I've seen go above him in recent mock drafts.


Noah Syndergaard, New York Mets

Solid SP2 with high-end SP2 upside

Thor seems to pull people in every year with those long, flowing locks and triple-digit fastball. However, he rarely matched expectations when it’s all said and done. That’s something you can take advantage of. As early ADP indicates, the fantasy public is souring on Thor as the failed expectations become part of the larger narrative. Yet, his underlying skills remain strong. He’s .280 xwOBA was 16th among pitchers who faced a minimum of 350 batters, and he also ranked in the top 20 in the difference between actual slugging percentage and expected slugging percentage, with an XSLG of .366 what would have also put him 16th last season. Part of the explanation for Syndergaard’s poor performance in relation to his expected stats is that the New York Mets ranked second-worst in DRS and seventh-worst in UZR, which means he got no help from his defense.

There were also many reports that he lost a feel for the slider, possibly due to the change in the ball. Thor's slider dropped from a 7.8 Pitch Value (pVAL) to a 1.7 pVAL pitch, which helped his swinging strike percentage (SwStrk%) also drop 1.1 points to 12.5%. However, the ball is reportedly being switched back and could lead to improved results in Thor’s slider, which would then be paired with a change-up that saw a pVAL jump to up 6.8 from 1.4 in 2018. Thor is by no means a sure thing, but his upside is still high enough that I would take him over Yu Darvish, Zack Greinke, and Tyler Glasnow, who are all going before him.


Matthew Boyd, Detroit Tigers

Low-End SP3

Boyd’s .297 xwOBA would have put him 33rd in the league, which gives him some room for improvement; however, I’m not as bullish on Boyd as I am on some of the other guys on this list. Boyd’s success and failure last year was heavily tied to his fastball velocity, which jumped from 91.1 MPH on average in 2018 to 92.4 in 2019. That, however, is still below average fastball velocity, and the pitch had a 1.9 pVAL; although that’s an improvement from the -0.5 the year before, it doesn’t give me confidence that he has newfound, consistent success with it.

There is some intrigue in the fact that he saw a jump in SwStrk% from 10.2 to 14.0, which can partially be tied to his improvement in getting hitters to chase outside of the zone. He had a 4.6% jump in O-Swing%, and batters swung at Boyd’s 1550 pitches and missed on 484 of them (31.2%) which is above league average (24.9%). Although his slider was markedly worse in pVAL than it was in 2018, it still has above-average results and movement. Boyd’s slider moves, on average, five inches towards a right-handed batter and drops 46 inches, while the league average horizontal movement is six inches and 39 inches drop. Improvement in 2020 from Boyd is supported by a 3.88 xFIP and those aforementioned jumps from last year; however, I would be cautious of expecting consistent production throughout the whole season, which makes Boyd more of a high floor, low-end SP3 for me.


Josh James, Houston Astros

SP3 (if he gets a rotation spot)

Nothing beats a post-hype sleeper. In the middle of last Spring Training, everybody was all over Josh James. He seemed like the next stud to grace the mound for the Houston Astros. Then he suffered a quad injury in spring and began the year in the bullpen, featuring dynamic raw stuff and the potential upside of a dominant arm, only for him to get hurt again in July and be placed on the IL with a shoulder strain. His numbers dipped a bit in July while pitching through the injury, but his overall underlying metrics tell us that his year was likely better than many think. James' .264 xwOBA would put him right in line with Snell, Mike Clevinger, and Stephen Strasburg. He also limited contact to a .171 xBA and a .286 xSLG, despite registering a .374 SLG on the season. His 16.2% SwStr% was near elite and his xFIP of 3.77 paints a much rosier picture than his 4.70 actual ERA.

When you match the numbers under the hood with a fastball that he can run up over 100 MPH, and three positive pVAL pitches, including a slider that has elite spin and above-average horizontal movement and drop, all the pieces are there for a Josh James breakout. With Jose Urquidy and Brad Peacock currently slotted into the number four and five spots in the Astros' rotation, James is not an unrealistic option to slide ahead of them. Urquidy has no Major League success and Peacock's best years came in the bullpen, so if James is able to snag a spot from one of them, we could see the true breakout we all wanted last year.


Mitch Keller, Pittsburgh Pirates

SP4 with SP3 upside

As it stands right now, Keller appears set to begin the 2020 season in the Pirates rotation. Despite mediocre results in his MLB debut, Keller proved to be a dynamic prospect as he worked his way up through the Pittsburgh’s minor league system. He possesses a strong fastball with elite spin, which tops out at 98, and has elite spin on his curve, which leads to two inches more drop than the league average. He also throws a slider that recorded a 27% SwStrk% and a 50.5 O-Swing%. Since we know he possesses good raw stuff, the fact that no pitcher in baseball underperformed their xwOBA more than Keller immediately jumps out.

When you look closer, you can see a few major culprits. For starters, he allowed a .475 BABIP, despite finishing with a .324 mark in AAA and .366 in AAA the year before and also had a sub-60% LOB rate. Both of these indicate that Keller was particularly unlucky or hurt by poor defense, a scenario that’s supported by his 3.19 FIP and 3.47 xFIP last year. I’d expect an ERA in the high three's with good strikeout numbers, which makes him an intriguing arm to breakout without having to draft him all that high.


Lucas Sims, Cincinnati Reds

SP4 (if he gets a rotation spot)

Damn the Reds and their solid addition of Wade Miley. The veteran lefty gives the Reds four locked-in starters: Miley, Trevor Bauer, Luis Castillo, and Sonny Gray, which means Sims could either compete for the 5th spot in the rotation with oft-injured Anthony DeSclafani or be moved back to the bullpen. For the purposes of this endeavor, let’s assume he gets a crack at the starting job in the preseason; there is a decent amount to like. In addition to the noticeable difference in expected stats in the table above, Sims’ improvement last year was borne out by a 4.12 FIP in AAA and the highest K% of his career at 30% during Triple-A and 32.2% in the Majors. He also saw a 12% drop in hard contact rate from 2018 and a drop in his walk rate by 6%.

He has three solid offerings with a fastball that moves 10 inches towards a right-handed batter despite a league average horizontal movement of 7 inches, a slider that has a horizontal movement of 12 inches despite a league average of 6, and a curve that moves 2 inches more horizontally than major league average. The curve also had a pVAL of 5.5 last year, which would have been good for 15th best had he qualified with the right amount of innings thrown. His 14.9% SwStr% would have put him 10th in the league behind Lucas Giolito, which suggests that strikeout upside is real. If he wins the job, he could be dinged a bit by pitching in a hitter’s park, but I see a path to a sub-4.00 ERA with good strikeout numbers despite a high walk rate.


Justus Sheffield, Seattle Mariners

End-of-Draft Stash

Once an (over) hyped Yankees prospect, Sheffield posted a 5.50 ERA in 36 innings with Seattle last year but a more concerning 6.87 ERA in Triple-A. However, he dominated AA with a 2.19 ERA, which gave some people cause for optimism. After all, Sheffield is still only 23-years-old, and the large discrepancy in his expected OBA versus his wOBA gives us a reason to dive in again. While a .321 xwOBA seems high on the surface, it puts him in the same boat as Pablo Lopez, Caleb Smith, Robbie Ray, and Chase Anderson. His xBA last year was .245 which came in well below his actual BAA of .303, and the difference in his SLG allowed and xSLG was also a sizable .092. Part of that has to do with him only giving up 16.5% hard contact, but it also feels a little fluky.

Sheffield has strikeout upside with a strong 22% K% during his major league stint last year. He has a 93 MPH fastball that he can get up to 95, which is solid from the left side, and pairs with that a slider that drops two inches more than league average that he throws 35.7% of the time. However, his ceiling is currently limited by only being a two-pitch pitcher. He has some untapped upside to emerge as an SP4 in fantasy leagues, but that’s not the type of upside that you’ll be cursing yourself for missing out on if somebody swoops before the last rounds of the draft.


Elieser Hernandez, Miami Marlins

Waiver Wire Watch

The beauty of fantasy baseball is that something we find value in the unlikeliest of places. Coming into the year, many people thought the Miami Marlins would be a glorified minor league team, but we all took turns falling in love with Caleb Smith, Pablo Lopez, and Zac Gallen (until he was traded). Is it time to add Elieser Hernandez's name to that list? Not likely. However, his expected stats can put him on our list of pitchers to watch in the early weeks of the season.

He had an elite exit velocity against, which helped him to an xBA of only .209, sizably lower than his .242 final numbers. He also pitched to a .391 xSLG, which was a full .112 points below the actual slugging percentage he allowed. It was his age 24 season and only his second crack at the major, but he dropped walk % by 2.1 points and raised his K % by a whopping 8.2 points. His slider had a 7 pVAL and has almost double the league average horizontal movement. At the end of the day, he's not going to win you fantasy leagues, but he's a young pitcher with an above-average SwkStrk who induces soft fly balls in a pitcher's park. It's not going to be pretty, but there is a scenario where it is useful.


Darwinzon Hernandez, Boston Red Sox

Who Knows?

I have no idea where Hernandez pitches for the Red Sox this season, but his underlying metrics suggest that he needs to be mentioned on this list. He had the fifth-largest discrepancy between xwOBA and wOBA of any pitcher with over 100 plate appearances against, and his .159 xBA trailed only Josh Hader with the same qualifications. What's most impressive is that the average suppressing was done with both of his most frequently used pitches. His fastball registered a .154 xBA, .269 xwOBA, and.205 xSLG), which his slider finished with a .177 xBA,.240 xwOBA, and .277 xSLG. He had a solid 13.7 SwStrk% and saw his K% finish at 38.8% in his first big league stint.

He currently seems slotted to begin the year out of the bullpen, but the Red Sox are also trying to move David Price to clear salary cap, which could free up a rotation spot for the 23-year-old. If he stays in the bullpen, there is a chance that he could work himself into high-leverage situations and perhaps become useful in SV/HLD leagues. Spring Training will be big for him, but he has the tools to be a strong fantasy contributor.

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Using Sabermetrics for Fantasy Baseball: HR/FB

Using BABIP to predict a player's batting average is great. Average is a category in many league formats and every hit is an opportunity to steal a base or score a run. But most owners find the long ball sexier.

Every HR comes with a guaranteed run scored and at least one RBI. Many owners build their teams around power for this reason. Yet fluky HR campaigns can happen just as easily as fluky batting average ones.

How do we tell the difference between a legitimate breakout and a fluke?


How to Interpret HR/FB

HR/FB measures the percentage of fly balls that leave the park. Last year, a power-friendly baseball contributed to 15.3% of all fly balls ending up in the seats. Like BABIP, an experienced player's personal benchmark in the stat is a better indicator of his future performance than the league average. For example, Cody Bellinger is generally regarded as one of the top sluggers in the game today. His HR/FB was 24.6% in 2019, significantly higher than the league-average rate. If this number regressed to the league average, Bellinger wouldn't be very good. However, he has a career rate of 21.8%. Clearly, above-average power is something Bellinger just does. He should continue to crush bombs with regularity.

Large spikes or dropoffs in HR/FB are generally temporary, meaning that the stat is usually not predictive of a power breakout. Fantasy owners want to know the next power breakout, so this may be somewhat disappointing. Future power production may be predicted, however, by an increase in fly ball rate, or the percentage of a batter's flies as opposed to liners or grounders. There are limits here, as Billy Hamilton is never helping a fantasy team with his power no matter how many fly balls he hits. Still, FB% is generally the stat you want to look at for power potential.


What Is a Good FB%?

Elite sluggers generally post a fly ball percentage of around 40%. Subjected to this test, Bellinger had a 42.4% fly ball rate in 2019 and a career mark of 42.9%. These rate stats, combined with a consistently above average HR/FB, make Bellinger the consensus top-round pick he is.

Bellinger doesn't really illustrate the distinction between HR/FB and FB% because he excels at both. For a predictive illustration, consider Aaron Judge of the New York Yankees. His HR/FB last season was an unbelievable 35.1%, powering fantasy rosters with a total of 27 long balls despite being limited to 447 PAs. Of course, you know that Judge has the potential for more if you remember his 2017 total of 52 homers.

Judge posted a 35.6% HR/FB in his stunning rookie campaign, virtually unchanged from his rate last year. The difference lies in his FB%, which passed the 40% test described above in 2017 (43.2%) but fell well short last year (32.4%). If his FB% remains low, he could disappoint owners expecting 40+ home runs in a full healthy season. Of course, you could also make the argument that Judge's FB% has fallen because of his injuries, in which case it will rebound as soon as he's truly healthy. Do you want to roll the dice?

Some of the other players who look primed for significant power regression in 2020 include Fernando Tatis Jr. (31.9% HR/FB, 30.9 FB%), Alex Avila (37.5% HR/FB, 25 FB%), and Shohei Ohtani (26.5% HR/FB, 27.9 FB%). Two of the three players listed here last year (Ian Desmond and Eric Hosmer) were clear fantasy busts, while Ohtani returns to the list after a second successful season with limited PAs. You can bank on a repeat if you want to, but consider yourself warned.



HR/FB is considered the BABIP of power because it can be used to evaluate whether a given player is outperforming his true talent level. A player with a large spike or decline in HR/FB should generally be expected to return to his established baseline moving forward. Ballpark factors may alter HR/FB, but in general raw fly ball percentage is a better tool to identify potential power breakouts.

Of course, it is possible for a batter to legitimately change his approach and permanently boost his HR/FB. Statcast allows us to measure precisely how hard a player is hitting the ball, potentially validating a performance that would otherwise be labeled a fluke. Check out some of our other introductory sabermetric articles by clicking on this link!

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Using Sabermetrics for Fantasy Baseball: An Introduction

Hello, fellow RotoBallers! Sabermetrics have become an integral tool for fantasy baseball draft prep, but a concise resource for understanding the basics can be difficult to find.

Over the next two months, this series will attempt to define and explain all of the metrics fantasy owners may find useful, citing examples of how to use them in the process. Multiple degrees in applied mathematics are not required to use advanced metrics effectively, and this will be a no-math zone. We also won't bring in some of the metrics that don't have as much fantasy relevance, most notably the fantasy-useless WAR, or Wins Above Replacement.

Instead, the focus will be on sabermetric statistics and ideas that are useful for predicting the standard stats the vast majority of fantasy leagues care about, such as batting average and home runs for hitters, and ERA and strikeouts for pitchers. If you're tired of finishing in the bottom half of your fantasy leagues, using these tools can give you a significant advantage on draft day and beyond. Here is a brief look at some of the concepts we'll be covering, as well as one central question we'll try to answer:


The Basics

Concepts in this category are relatively common knowledge in the fantasy baseball community, so it's safe to assume that at least some of your rivals are using them. That said, you have to learn how to walk before you start running. If you're just getting into fantasy baseball, these articles represent a great entry point.


What can a player do to influence his batted ball "luck?"


What is the best way to determine if a power breakout was real or a fluke?

Batted Ball Distribution

Grounders, fly balls, line drives: which one is the best for fantasy production?

Plate Discipline

Why should I care about plate discipline if I play in a standard 5x5 roto league?


Is it possible to measure a pitcher's performance on his own merits, separate from his teammates?

BABIP (Pitchers)

Can we quantify the support a pitcher's fantasy production receives from his teammates?

Trickier Concepts

Concepts in this category seem easy enough, but involve some nuance that can trip newcomers up. Considering that interpreting advanced stats incorrectly is often worse than not using them at all, some fantasy owners limit themselves to just the concepts above. Of course, that means that you can start to gain a competitive advantage by reading the articles below.


Are all pulled batted balls the same?

Batting Order

Which role is more valuable: the Yankees 8th hitter or Baltimore's cleanup man?

Pitch Info

How can we tell if a pitcher's repertoire shift supports his breakout?

Ballpark Factors

How much do ballparks really affect a player's fantasy stats?

MiLB Stats

Is my team's top prospect the next big fantasy contributor?



If you've watched a baseball game recently, you've probably heard announcers waxing poetic on the exit velocity of a home run or an outfielder's route efficiency to a ball. Those singular events don't have much value in fantasy baseball, but Baseball Savant also compiles average statistics that give fantasy owners more info to work with than they have ever had before. This category is where you'll find most of the innovation in the space, as well as the biggest competitive advantages.

Exit Velocity

Who hits the ball with the most authority on a consistent basis?


Why does adding launch angle make exit velocity into a more effective predictive tool?

Pitcher Statcast

Can pitchers really influence what happens once a ball is put in play?

Spin Rate

Do fantasy owners prefer to see a high spin rate or a lower one?


What are some of the advantages and disadvantages of Baseball Savant's projected BA and SLG?



Advanced stats can do a lot more than what's listed above, but these concepts are more than enough to help you start using analytics to make smart fantasy baseball decisions. You'll see Rotoballer analysts using the metrics above on a regular basis, so read some articles and get a feel for how to do your own analysis. Our site's live chat is also a great place to ask questions and solicit opinions from the fantasy community at large. Use the information above to dominate your leagues in 2020!

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Exit Velocity Pitching Leaders - Statcast 2019 Review

Ah, winter, the true dead zone of baseball coverage. The new champs have been crowned, vacation plans are made, the awards are being handed out and the winter meetings getting underway. Sounds like the perfect time to review some Statcast data in preparation of 2020 fantasy drafts.

Exit velocity has become one of the more commonly-known advanced metrics among the common fan and while it's mostly used for hitters, there's much to be learned by analyzing what pitchers are giving up the least hard contact as well. The batter typically has more say in how hard a ball is hit, but the pitchers below have shown a strong ability to limit hard contact over a large sample size.

To keep things focused on starters, we'll mostly be looking at a sample size of 129 pitchers that experienced at least 300 batted ball events (BBE) in 2019. The median exit velocity among those pitchers is 88.0 miles per hour which is the exact same as it was in 2018, so we have a consistent baseline to work with. To read about the exit velocity and barrel leaders among hitters, click here.


Ryan Yarbrough, Tampa Bay Rays

Atop the exit velocity leaderboard sits Ryan Yarbrough, who absolutely dominated the category in 2019. Yarbrough's 84.1 average exit velocity was a whopping 1.1 mile per hour better than the next closest pitcher, a big difference when you consider the range between the top and bottom pitcher is just 6.7 MPH. 2019 wasn't a fluke for Yarbrough either, he was 11th among pitchers in 2018 with an 85.5 average exit velocity so this is now two straight years and over 850 BBE where the 27-year-old lefty has been elite at allowing soft contact.

Yarbrough should be a popular breakout pick heading into 2020 as his 3.55 FIP was much better than his 4.13 ERA. He also improved in the second half of the season, upping his strikeout rate by four percent over the first half and pitched to a 3.79 ERA and sub-1 WHIP after the All-Star break. Yarbrough is a pitcher that can be drafted in the double-digit rounds but can make a big impact on your fantasy team.


Kyle Hendricks, Chicago Cubs

Kyle Hendricks was Mr. Consistency in 2019 as his 85.2 MPH average exit velocity was the same as it was in 2018. In fact, Hendricks has been among the most reliable pitchers in the game in this department for some time; he has finished eighth or better in average exit velocity every season dating back to 2016. Hendricks is among the most extreme contact pitchers in the game as he doesn't generate a ton of strikeouts or walks. He generally keeps the ball on the ground and has proven his extreme soft contact numbers aren't a fluke as he's been elite in the category four straight years.

Hendricks won't blow anyone away with a gaudy strikeout total, but he can still be a consistent, know-what-you're-getting starting pitcher that any fantasy manager would be happy to have as a middle-of-the-rotation starter that can be drafted in the middle rounds.


Brandon Woodruff, Milwaukee Brewers

Of all the pitchers atop the average exit velocity leaderboard, Brandon Woodruff is the one that seems to have the most actionable data. His 2.6 percent barrels per plate appearance was easily the best rate in the Majors, meaning he showed a strong ability to avoid the sweet-spot of the bat. Additionally, while his 85.6 MPH average exit velocity was sixth in the league, he was second in baseball in exit velocity on fly balls and line drives, something that is very important pitching in homer-friendly Miller Park.

Woodruff struck out 10.6 batters per nine innings last season and his 3.36 xFIP was better than his 3.62 ERA. Despite an average-looking 3.62 ERA, he is on the short-list for pitchers poised to have a breakout season in 2020.


Zack Wheeler, New York Mets

Wheeler has been in the news because of his $118 million deal with the Phillies. They must have already known that he was on the leaderboard in this category. His average exit velocity actually went up from 2018 to 2019 but he still finished 11th in the category after finishing fourth in 2018, so he's now put up back-to-back seasons being among the best at avoiding hard contact. Like Woodruff, Wheeler was also even better on balls in the air as his average exit velocity on fly balls and line drives ranked fifth in the league after being second in that department in 2018.

Wheeler was top-five in the Majors in hard contact rate and shows no reason he can't carry his success into the new year, especially since he stays in the same division. In addition to two years of positive Statcast data, Wheeler struck out a batter per inning last season and his 3.48 FIP was better than his 3.96 ERA. Wheeler should be in the SP3 mix come draft season with upside to finish much higher if he can put up close to the 195 1/3 innings he threw this past season.


Julio Urias, Los Angeles Dodgers

Julio Urias pitched mostly out of the bullpen last season and therefore doesn't quite make the 300 BBE threshold we've been using. However, it would be remiss not to mention the average exit velocity leader in this space. In 209 BBE, Urias allowed an average exit velocity of just 83.2 MPH, almost a full mile per hour better than Yarbrough who was already way ahead of the pack. Urias was the only pitcher with at least 200 BBE to allow fewer than 25 percent of them to be hit 95+ MPH.

Yes, pitching out of the bullpen is typically considered easier as pitchers can exert more force into each pitch, but Urias still had a great season by the Statcast metrics. Throw in a 26.1 percent strikeout rate and it's clear why Urias is considered a top pitching prospect. The pitching-rich Dodgers are always going to cycle through starting pitchers, but Urias should get his chance to start this season and when he does, he's a guy fantasy managers will want on their squad.

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Barrel and Exit Velocity Batting Leaders: Statcast 2019 Review

The 2019 season saw more home runs hit than ever before in Major League history as 6,776 balls left ballparks across the league. The top-hitting club of them all was the Minnesota Twins with 307 big flies. Chief among them was none other than 39-year-old Nelson Cruz, who finished in a tie for seventh in all of baseball with 41 HR. If you pay attention to Statcast, this should come as no surprise. Cruz led the majors in Barrel rate with a barreled ball in 12.5% of his plate appearances and was third in average exit velocity. Rookie Peter Alonso, who led the majors with 53 homers, was tied for 14th in Barrel rate. Studs like Mike Trout, Aaron Judge, and Christian Yelich all appear in the top 10 as well.

This comes as common sense to even the most casual baseball fan, since hitting the ball harder will typically result in more home runs and great players tend to do just that. Those who followed our Statcast Hitter Analysis series all season will know that not all names atop the leaderboard are expected. Some players have less fortunate luck on balls in play or have less-than-ideal ballpark factors or lineup support.

The purpose of our year-end review will be the same as during the season - to identify surprising names across the leaderboard for both barrels and exit velocity in order to identify possible sleepers for 2020. For full explanations of each term, refer to the official Statcast glossary definitions for exit velocity, and barrels by clicking the links.


Exit Velocity Leaders

Stats taken from and represent hitters with a minimum of 50 batted ball events in 2019.


Mike Ford (1B, NYY) - 91.9 MPH AVG (19th)

This former Rule 5 pick, who was sent back to the Yankees just three months after being selected by Seattle in 2017, made the most of his late-season playing time in place of an injured Edwin Encarnacion. He hit 12 bombs in 143 at-bats before ultimately being left off the postseason roster. He showed good plate discipline too, with a 6.8 K-BB% and an 8.1% SwStr%. Given the way Luke Voit hit much of the year and the fact that the Yankees can and likely will do better in the free-agent market, Ford's path to success lies with another team if he finds a favorable spot.


Yandy Diaz (3B, TB) - 91.7 MPH AVG EV (23rd)

It was all of two weeks into the 2019 season when Yandy Diaz began his offensive breakout at the age of 27. He batted .308 with three homers in the first 10 games of the year and would go on to hit 14 HR in 307 at-bats before a move to the 60-day IL cut his season short. He made quite a return with two solo homers in the Rays' first playoff game before experiencing foot soreness again. If Diaz can come back fully healthy and picks up where he left off, who's to say 30 homers are out of reach? That's probably overly optimistic but Diaz is a nice discount CI who is currently going outside the top 200 in early NFBC drafts (below Jon Berti!).


Jason Castro (C, FA) - 91.5 MPH AVG EV (28th)

I love when catchers make these lists because it always gives us a shred of hope that they could actually bring value to fantasy rosters beyond two-catchers leagues, which really ought to be outlawed. So often their plate appearances are too limited to be meaningful anyway. In this case, Castro only played 79 games for the Twins because he took a backseat to breakout player Mitch Garver who also ranks in the top-50 on the exit velocity leaderboard. Castro is now a free agent and could find a home where he gets semi-regular ABs. He's only a career .231 hitter but Castro should still have double-digit homers and 20+ doubles left in his bat, which isn't too shabby for a backstop these days.


Barrel Leaders

Stats taken from and reflect hitters with a minimum of 50 batted ball events.


C.J. Cron (1B, FA) - 10.6% Brls/PA (7th)

Just when he'd found the perfect home, Cron now has to search for another through free agency. Despite being on a record-setting Twins lineup, Cron didn't match his career-high of 30 HR. He settled for 25 but did barrel the ball at a rate of 15 Barrels per BBE, or the same as Ronald Acuna and slightly higher than Bryce Harper. Cron saw a sudden three-point jump in exit velocity last season which could be seen as an outlier. His barrel rate had been increasing for three straight years though, thanks to a reduced launch angle. He also has brought his walk rate closer to respectable levels while whiffing less, so it could be a matter of experience setting in. He'll be a valuable slugger if he lands a regular DH gig somewhere.


Adam Duvall (OF, ATL) - 10.0% Brls/PA (11th)

There aren't many better comeback stories than Duvall last year, statistically speaking at least. After toiling at Triple-A nearly through the end of July, Duvall was brought up by the Braves and immediately began raking. He hit five homers in his first six games and provided an instant spark to the Braves offense. His admittedly small sample size of 120 at-bats last season could be dismissed as a hot streak except that Duvall is a former All-Star with back-to-back seasons of 30+ HR and 99+ RBI. Of course, he's on the wrong side of 30 now and not guaranteed to start for the contending Braves, although he was recently tendered a contract. He should serve a utility role at the very least and is worth owning in NL-only and deep leagues requiring five OF based on his power ceiling.


Brandon Lowe (2B, TB) - 9.2% Brls/PA (21st)

Another Ray who missed a chunk of time due to injury, Lowe earned an All-Star berth in his rookie season thanks to his power output. Lowe hit .276 with 16 HR, 49 RBI, and 40 runs in the first half. He placed 33rd on the Sweet Spot leaderboard with a 40.2% SwSp% and a solid .506 xSLG supports the idea that he can carry on as a top-10 option at the keystone for fantasy owners in roto leagues. He'll need to work on his strikeout rate, which was 2.8 percentage points above the league average, and play solid defense to stay in the Rays' infield long-term but there's no reason to think he won't do so.

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Statcast Pitcher Studs and Duds - wOBA-xwOBA Difference for Week 26

Welcome back to RotoBaller’s pitchers Statcast studs and duds article series for the final week of fantasy baseball! Each week we will select an advanced stat, choose two studs and two duds, and analyze what those stats could mean for future fantasy output.

We have sadly reached the end of the season, so I thought it would be interesting to go back to a metric that I used earlier in the season for it's predictive nature; wOBA-xwOBA. Now that the season is all but over, we can take a look at who ultimately outperformed and underachieved compared to what was expected of them.

Pitchers should perform towards their expected metrics over the course of the season, but it doesn't always line up that way. Identifying players who did not align with their expected metrics should be a fun (or frustrating) way to cap off the fantasy season. So without further ado, let's get started on the last week's article!


wOBA - xwOBA Difference Studs

For reference, the league-average wOBA against is .324 and the xwOBA is .318 (difference of .006). All stats current as of Monday, September 23, courtesy of


Matthew Boyd - Detroit Tigers

wOBA: .324, xwOBA: .298, Difference: .026

Our first wOBA-xwOBA stud caused quite the ruckus at the beginning of the season due to his hot start and high strikeout numbers. The strikeouts turned out to be legit, as Matthew Boyd has maintained an impressive 30.5% K rate over 181 1/3 innings pitched this season. The other metrics maybe not so much, as his 4.57 ERA and 1.22 WHIP have been mediocre at best. All that being said, the difference between his wOBA and expected wOBA suggests that he has been quite unlucky and should have had a solid wOBA compared to the rest of the league. Let's see if we can pinpoint where the bad luck came in for Boyd.

The interesting thing here is that nothing really stands out as a culprit. Boyd's 1.22 WHIP and 6.4% walk rate are both respectable and in line with his career numbers (1.32 and 7.4%). Further, his .304 BABIP is only slightly higher than his career .296. Looking into his batted-ball profile, his 18.5-degree launch angle is pretty high, but his 88.7-MPH average exit velocity and 35.6% hard-hit rate are middle-of-the-road.

The clear positives of Boyd's season have been his insane K rate (thanks to his filthy slider) and his SIERA; his 3.59 SIERA indicates that he has gotten quite unlucky based on his batted-ball results. The ultimate takeaway here is that, while he should have actually seen better results, Boyd helped out fantasy owners all season long due to his high strikeout numbers. While his current team doesn't help his value, he has shown that he can be relied on as a fantasy asset.


Noah Syndergaard - New York Mets

wOBA: .302, xwOBA: .279, Difference: .023

Our second wOBA-xwOBA stud has been a fantasy stud for several seasons but could only muster average numbers in 2019 despite having an above-average wOBA. Noah Syndergaard has gone 10-8 with a 4.22 ERA, 1.22 WHIP, and 24.1% K rate. Unfortunately for fantasy owners, they almost certainly overpaid for him in single-season leagues. However, for those who have him in keeper or dynasty leagues, his xwOBA gives hope that he can rebound next season. Let's take a further dive into Thor's 2019 season. 

Like Boyd, Syndergaard presents somewhat of a puzzling case. His WHIP and walk rate (6.2%) were respectable and his .308 BABIP was actually slightly lower than his .313 career mark. His batted-ball profile was quite good; his average exit velocity (86.6 MPH) and hard-hit rate (31.9%) are both in the top 17% of baseball. Further, all of his expected stats (batting average, slugging percentage, wOBA) were above average, increasing the evidence for bad luck.

Like the Mets' season overall, things didn't go quite as planned for Thor this season. The good thing is that he showed many signs of still being a higher-end pitcher. I would expect some positive regression for Syndergaard next season and, hopefully, he can give fantasy owners more of what they had hoped for.


wOBA - xwOBA Difference Duds

For reference, the league-average wOBA against is .324 and the xwOBA is .318 (difference of .006). All stats current as of Monday, September 23, courtesy of


Mike Soroka - Atlanta Braves

wOBA: .270, xwOBA: .304, Difference: -.034

Our first wOBA-xwOBA dud has been excellent this season and is just 22 years old. Mike Soroka has gone 13-4 with an impressive 2.60 ERA, 1.09 WHIP, and 19.9% K rate while pitching to contact. However, his xwOBA, while still quite good, is significantly higher than his actual wOBA. Should fantasy players be worried about negative regression for Soroka next season?

Fortunately, it seems like Soroka's pitching style will allow him to continue to succeed at the big-league level. He relies heavily on his sinker (45.2% usage) and pitches to contact, but has solid control (1.09 WHIP, 5.7% walk rate). Further, his batted-ball profile has the makings of a successful groundball pitcher. Soroka has avoided hard contact (87.2-MPH average exit velocity, 37.9% hard-hit rate) while doing an excellent job of keeping the ball on the ground (5.4-degree launch angle).

The big negative is Soroka's SIERA. His 4.30 SIERA is almost two runs higher than his ERA. While I do feel that is is not realistic to expect a 2.60 ERA from Soroka next season, I also feel that his batted-ball profile is one that will lead to success. Therefore, I am going to overlook his SIERA and say that Soroka will be a higher-end fantasy option next season and for seasons to come in keeper/dynasty leagues.


Yonny Chirinos - Tampa Bay Rays

wOBA: .287 , xwOBA: .316 , Difference: -.029

Our second wOBA-xwOBA dud has served time both as a starter and a "follower" this season, finding success at both. Yonny Chirinos has gone 9-5 with a 3.67 ERA, 1.06 WHIP, and 22.1% K rate over 127 2/3 IP this season. He has been highly useful in fantasy, but does his relatively higher xwOBA suggest that he may regress next season?

Like Soroka, Chirinos relies heavily on his sinker (55.1% usage). However, his stats under the hood do not look as shiny. Chirinos' batted-ball profile isn't bad (87.6-MPH average exit velocity, 33.8% hard-hit rate), but his 10.9-degree launch angle is a little high for someone who relies on thier sinker so much. Further, his .252 BABIP is much lower than his .272 career mark. The Rays are one of baseball's better defensive teams, but that alone does not explain his BABIP. As such, I would be more inclined to believe his 4.21 SIERA.

Overall, Chirinos has been great this season and holds extra fantasy value given his relief pitcher eligibility. However, there is compelling evidence to suggest that he has gotten lucky this season. While he will still be valuable next season, I would not be surprised to see his ERA slide closer to 4.00 in 2020.


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Starters WAR Studs and Duds: Week 25

Welcome back to RotoBaller’s pitchers advanced stats and StatCast studs and duds article series! Each week we will select an advanced stat, choose two studs and two duds, and analyze what those stats could mean for future fantasy output. At this point in the season, I will focus on an all-encompassing stat for starting pitchers; wins above replacement (WAR).

WAR is an interesting metric that was developed as an attempt to measure a player's relative value compared to a replacement-level player, or a readily-available player (think free agent). The calculation is rather complicated and is broken down with further explanation here for those who are interested.

I will take a look at two players with an impressive WAR and two with a disappointing WAR to see which pitchers have been reliable and which have been difficult to trust. For reference, the highest WAR among qualified starters is Gerrit Cole's 6.5. I will take a look at some interesting names to help fantasy players make tough decisions about who to start with a fantasy title on the line.  


WAR Studs 

All stats current as of 9/16/19


Lance Lynn - Texas Rangers

(14-10, 3.72 ERA, 1.25 WHIP, 6.2 WAR)

Our first WAR stud has had a solid season and has a WAR higher than many better-known fantasy names. Lance Lynn has rebounded after a poor 2018 season, posting solid numbers across the board. Let's take a look under the hood and see how Lynn has found his success.  

The first thing that stands out has been Lynn's solid control. His 1.25 WHIP is much more in line with his career 1.31 mark than 2018's 1.51. Consequently, his batted-ball profile has been great. Lynn's 87.2-MPH average exit velocity and 33.1% hard-hit rate are both in the top 30% of baseball and he has kept the ball on the ground for the most part with a launch angle of 13 degrees.

The second thing that stands out has been Lynn's strikeout numbers. Lynn has made his way into my Strikeout Rate Risers article several times and has a 27.1% K rate with an 12.3% swinging-strike rate. Lynn relies mostly on fastballs (51.6% four-seamer, 16.8% sinker, 15.2% cutter), but has been able to get swings and misses thanks to the movement he has gotten on those pitches. Specifically, the spin rates on his fastball and cutter are in the top 10% of baseball. His movement combined with a high level of control has allowed Lynn to miss bats as well as avoid hard contact.

Lynn has been a standout fantasy option but faces a tough Astros matchup on the road this week. I could understand if fantasy owners did not want to risk it by benching him, but, given how much he has helped teams all season, I would not have a problem starting him this week.


Noah Syndergaard - New York Mets

(10-8, 4.15 ERA, 1.07 WHIP, 4.2 WAR)

Our second WAR stud has not provided what fantasy owners were hoping for this season but still has a high WAR. Noah Syndergaard has a mediocre 4.15 ERA and a 24.2% K rate this season; however, his WAR indicates that he has been one of the most valuable starters. Let's take a look at how these conflicting numbers can exist. 

The main thing that is helping Syndergaard is his FIP. His 3.53 FIP is much better than his ERA, indicating that he has gotten unlucky. This jives with his batted-ball profile; Thor's average exit velocity and hard-hit rate are both in the top 15% of baseball and he has kept the ball on the ground with a 9.4-degree launch angle. His SIERA doesn't quite back all of this up though. His 4.07 SIERA suggests that, despite his batted-ball profile, his results have been what they should be.

It has been an interesting season for Syndergaard, which has made thing frustrating for fantasy owners. He has allowed at least four runs in three of his last four starts and has to go to Coors Field this week. It would be a very tough decision to sit him this week, but I wouldn't blame owners for doing so.  


WAR Duds

All stats current as of 9/16/19


Wade Miley - Houston Astros

(14-5, 3.71 ERA, 1.31 WHIP, 1.9 WAR)

Our first WAR dud has a lowly WAR despite being a huge fantasy contributor. Wade Miley has completely restarted his career with the development of his cutter and has been contributing on a competitive Astros team. His numbers have regressed a bit over the course of the season, but he still has only the 49th-highest WAR among qualified starting pitchers. Should this fact impact fantasy owners' decisions on whether to start him or not for the final few weeks? 

Let me quickly reassure those that have Miley; he may not be the sexiest fantasy option as we all know, but he has been a solid pitcher this season. I will first point out the aspects of Miley's game that may contribute to his low WAR. First, he has not pitched all that deep into games. Miley has averaged roughly 5 1/3 innings per start, which isn't awful but isn't great. Second, he has had relatively low strikeout numbers. The switch to the cutter has helped him immensely but is not conducive to striking out hitters. His 19.9% strikeout rate isn't poor, but it is hard to rack up strikeouts when you throw an 87-MPH cutter 47% of the time.

The biggest factor in Miley's low WAR has been his high FIP (4.42). This stat, along with his SIERA (4.76), suggests that Miley is overachieving and may be benefitting from his team's defense rather than his actual skills. However, there is conflicting evidence to support Miley's performance. His batted-ball profile has been strong this season (87-MPH average exit velocity, 32.1% hard-hit- rate, 7.8-degree launch angle).

Miley had thrown a few poor games recently but rebounded nicely in his last start. He will face a Mike Trout-less Angels this week; given his overall season performance, I would be starting Miley.


Yu Darvish - Chicago Cubs

(6-6, 3.97 ERA, 1.11 WHIP, 2.0 WAR)

Our second WAR dud had a slow start to the season but has been providing more of what fantasy owners have expected lately. Yu Darvish now has respectable stats, but his WAR sits below many other starters. Should this matter down the stretch?

The bottom line is that Darvish has pitched extremely well lately. His 2.01 ERA, 0.80 WHIP, and 2.52 SIERA over his last five starts indicate that everything is clicking. Further, nothing really stands out in his season stats. He has managed to last about 5 2/3 innings per start, which isn't great but isn't awful. Further, his batted-ball profile has been slightly below average on the season. However, everything else has looked fine, especially of late.   

Darvish has figured things out and he looks like a higher-end fantasy asset once again. He has a two-start week, taking on the Reds in a decent matchup and the Cardinals in a tougher one. Regardless, I would not hesitate to use Darvish this week, despite his WAR.

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Pitcher Advanced Metrics Studs and Duds: WHIP for Week 24

Welcome back to RotoBaller’s pitchers Statcast and advanced metrics studs and duds article series! Each week we will select an advanced stat, choose two risers and two fallers, and analyze what those stats could mean for future fantasy output. With the fantasy playoffs upon us, I will write about a broader stat that can influence other aspects of a pitcher's game: walks and hits per inning pitched, or WHIP.

While WHIP is more of a general stat, it ties into many other more advanced stats. WHIP is of course made up of walks and hits, so it is important to examine both walk rate as well as BABIP in tandem. Generally speaking, pitchers with higher WHIPs have worse command and are therefore more in danger of poor starts.

At this point in the season, nitty-gritty stats aren't quite as helpful as a general picture of how a pitcher is performing, hence my choice of stat for the week. Let's take a look at some pitchers' WHIPs in hopes of better-understanding how they may perform in their last few starts of the season!


WHIP Studs

All stats current as of Monday, September 9, courtesy of


Jack Flaherty - St. Louis Cardinals

(10-7, 2.99 ERA, 1.03 WHIP) 

Our first WHIP stud started the season as a promising young starter yet did not provide what owners were hoping for. However, Jack Flaherty has rebounded throughout the course of the season and now owns a 2.99 ERA and 1.03 WHIP, one of the lowest among qualified starters. Let's take a deeper look at Flaherty's WHIP and how he has found success.

Flaherty checks off all the boxes behind his WHIP that support his success. First, he has kept his walk rate in check at a respectable 7.3%. Second, he has managed a solid .253 BABIP, which is in line with his .260 career mark. Finally, his batted-ball profile supports his BABIP; his average exit velocity (86.4 MPH) and hard-hit rate (32%) are in the top 14 and 19 percent of pitchers. Couple that with a high-end strikeout rate and you have all the makings of a fantasy star.    

Flaherty has been great for most of the season, thanks to his solid control. He has been a huge fantasy asset this season and should remain in owners' lineups for all matchups down the stretch.


Sonny Gray - Cincinnati Reds

(10-6, 2.75 ERA, 1.10 WHIP)

Our second WHIP stud has had quite the rebound season. Sonny Gray was awful last season with the Yankees but is having a career season in many ways with the Reds, posting a stellar 2.75 ERA, 1.10 WHIP, and a career-high 28.5% strikeout rate. Gray has been able to keep his WHIP low for a good portion of his career thanks to his groundball style of pitching, but how has he made such a quick turnaround from 2018?

Gray's 9.4% walk rate isn't awful, but it isn't why his WHIP is so low. The main contributor seems to be his .261 BABIP, which is a good deal lower than his career .281 mark. It's interesting that his BABIP has been so solid given his move to a hitter-friendly ballpark. However, his low BABIP is backed up by his batted-ball profile, as both his average exit velocity and hard-hit rate are in the top 28 percent of baseball. 

Gray has managed to find great success all season long and should continue to find it this week at the Mariners. Like Flaherty, he should be a no-brainer start for the rest of the season. 



All stats current as of Monday, September 9, courtesy of


Dakota Hudson - St. Louis Cardinals

(15-6, 3.40 ERA, 1.41 WHIP)

Our first WHIP dud is a teammate of Flaherty and has also performed well this season despite being young. Dakota Hudson has been a solid piece in the Cardinals' rotation throughout the season, in spite of having one of the highest WHIPs among qualified starters. Should this concern fantasy owners as we enter the later rounds of the playoffs?

Hudson's walk rate isn't the culprit behind his high WHIP; his 10.5% isn't great, but isn't terrible. His .279 BABIP is also manageable, although his 4.98 SIERA suggests that he has gotten lucky throughout the season. His average exit velocity (88.7 MPH) and hard-hit rate (38.9%) are both below league average. What saves him is his extremely-low launch angle at 2.9 degrees. Hudson is a sinker-ball pitcher and has done it well, hence his strong peripherals.

Hudson's peripheral stats are solid, but his underlying numbers send mixed signals. The good news is that he has kept the ball on the ground, limiting the damage of the hits he gives up. The bad news is that he will be on the road against the Rockies this week, where it is quite difficult to keep the ball out of the air. Despite his performance this season, I would be afraid to start him this week.

Eduardo Rodriguez - Boston Red Sox

(17-5, 3.81 ERA, 1.36 WHIP)

Our second hard-hit rate stud has put together a solid season overall, posting a 3.81 ERA and 23% strikeout rate. The one sore spot that stands out for Rodriguez is his high WHIP. Should this concern owners down the stretch?

Rodriguez's walk 9% walk rate is not the culprit for his high WHIP. He also has an excellent batted-ball profile, with both his average exit velocity and hard-hit rate in the top eight percent of baseball. The perpetrator seems to be his elevated .311 BABIP compared to a .297 career mark. It seems as though E. Rod is getting unlucky on balls in play, which, while certainly unfortunate, is a more-welcome explanation than him pitching poorly. 

Rodriguez has been pretty reliable for most of the season and gets a two-start week. He will have to face the Yankees and Phillies on the road, which are both tough matchups. However, I would be willing to trust him despite his WHIP issues. 

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Statcast Pitcher Studs and Duds: Hard-Hit Rate for Week 23

Welcome back to RotoBaller’s pitchers Statcast studs and duds article series! Each week we will select an advanced stat, choose two risers and two fallers, and analyze what those stats could mean for future fantasy output. I wrote about this stat in week 18 and it is one that can help indicate overall pitcher performance; that stat is hard-hit rate.

Hard-hit rate is defined as a ball hit at least 95 MPH. The reasoning behind that mark can be found here. It is important to note that exit velocity is a better stat for hitters than pitchers because hitters have a greater influence on the measure. That being said, hard-hit rate (and batted-ball profile overall) is very important for pitchers. Generally speaking, pitchers don’t want to give up hard contact as it improves the hitter’s chance of getting a hit.

The fantasy playoffs are upon us and each start is very important. Getting hit hard increases the chance of a poor start, so it is important to identify who has avoided hard contact. Let's get going identifying two hard-hit studs and two hard-hit duds!


Hard-Hit Rate Studs

All stats current as of Monday, September 2, courtesy of


Anibal Sanchez - Washington Nationals

(8-6, 3.80 ERA, 1.32 WHIP, 27.1% hard-hit rate) 

Our first hard-hit rate stud, despite his age, has put together his second consecutive strong season and currently has the lowest hard-hit rate among pitchers with at least 400 batted-ball events. 35-year-old Anibal Sanchez has a 3.80 ERA and 1.32 WHIP with a mere 27.1% hard-hit rate. Let's see how the veteran has found his success. 

Sanchez presents a bit of a puzzling case here. His batted-ball profile has been excellent overall as it was last season (86.3-MPH average exit velocity, 14.5-degree launch angle). However, Sanchez's arsenal is nothing special; he doesn't throw hard (fastball velocity is in the bottom seven percent of baseball) and his offspeed pitches don't have a ton of spin on them. He does throw a cutter, split-finger fastball, and sinker, yet his launch angle is not that of a groundball pitcher. Finally, his 1.32 WHIP and 8.8% walk rate do not indicate that he has had pinpoint command, which you would think would be needed to avoid hard contact without great pitches.

Sanchez currently has a 5.05 SIERA, which makes things even more puzzling. However, at this point in the season, I think he will continue to outperform his SIERA. He will face a surging Mets offense this week, but, given his performance all season long, I would be starting him.


Eduardo Rodriguez - Boston Red Sox

(16-5, 3.97 ERA, 1.36 WHIP, 28.4% hard-hit rate)

Our second hard-hit rate stud has put together a solid season throughout, posting a 3.97 ERA, 28.4% hard-hit rate, and 22.8% strikeout rate. Eduardo Rodriguez has had some control issues this season but has pitched much better over the past 30 days with a 2.78 ERA, all the while avoiding hard contact. Let's take a look at E Rod's performance.

While his control has been poor at times, he has managed to post decent strikeout numbers and has avoided damaging contact (28.4% hard-hit rate, 85.7-MPH average exit velocity, 8.5-degree launch angle). His 2.78 ERA of late has been solid, but his 1.35 WHIP and 4.87 SIERA indicate that he has outperformed himself and has gotten lucky.

Like Sanchez, Rodriguez has shown some conflicting signs throughout the season. However, given the upside he has shown and the strong team he pitches on, I would continue to rely on him this week, even against a tough Twins matchup. 


Hard-Hit Rate Duds

All stats current as of Monday, September 2, courtesy of


Shane Bieber - Cleveland Indians

(12-7, 3.278 ERA, 1.01 WHIP, 44% hard-hit rate)

My battle trying to understand this pitcher continues. Shane Bieber has been fantastic this season in terms of his peripherals and strikeout numbers. However, he also has a bunch of not-so-great underlying stats, including his hard-hit rate; Bieber's 44% mark is fifth-highest among pitchers with at least 400 batted-ball events. Given all the positives, should fantasy owners even think of questioning sitting him in the playoffs?

The concerns I have voiced throughout the season regarding Bieber still hold true. His pitch arsenal in itself isn't all that impressive; his fastball sits at 93.1 MPH and his slider and curveball don't have a ton on spin on them. Despite this, he has managed a strong 31% strikeout rate. I still haven't been able to find a good explanation for this, but feel like this should continue given its track record.

Further, Bieber had managed to keep his ERA and WHIP down despite a poor batted-ball profile. He has gotten hit quite hard this season (90.4-MPH exit velocity, 11.8-degree launch angle). Even more puzzling is his 3.30 SIERA, which measures a pitcher's individual performance with batted-ball profile in mind.

I am done questioning Bieber. He has been great despite contradictory underlying stats and fantasy owners have gotten to the playoffs because of him. He'll face the White Sox this week, who have been hitting well lately, but I consider him to be matchup-proof for the rest of the season.


Madison Bumgarner - San Francisco Giants

(9-8, 3.62 ERA, 1.09 WHIP, 41.1% hard-hit rate)

Our second hard-hit rate dud is a veteran who has been a fantasy staple for many seasons and is still getting it done. Madison Bumgarner has been solid for the Giants this season, particularly in the second half of the season. However, his hard-hit rate is in the bottom 13 percent of the league. Is this something that fantasy owners should be worried about down the stretch?

There isn't a ton of analysis to be done here; simply put, Bumgarner has been one of baseball's best pitchers for a long time and can be trusted, especially when it matters most. His command has been there (1.09 WHIP, 5.1% walk rate), his velocity has bounced back some since last season (91.4-MPH fastball), and he has continued to rack up strikeouts (24.9% strikeout rate). His batted-ball profile isn't great, but he pitches his home games in one of the best pitcher-friendly parks, which helps mitigate the results.

Overall, MadBum is a fantasy player that always provides in the clutch. He'll face the Cardinals on the road this week, a mediocre matchup, but I would never consider sitting him if I owned him. 

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Pitcher Studs and Duds - BABIP for Week 22

Welcome back to RotoBaller’s pitchers risers and fallers article series! Each week we will select an advanced stat, choose two risers and two fallers, and analyze what those stats could mean for future fantasy output. I have written about this stat several times in various capacities this season, but it is worth revisiting now that we have much more data and clear trends. That stat is BABIP, or batting average on balls in play.

BABIP is rooted in three main components: defense, luck, and talent. Two of these three pieces are out of players' control, so BABIP can cause deviations between expected and reported outcomes. By looking at pitchers with higher or lower BABIPs compared to their career marks, we can identify players who are more likely to see regression the rest of the season.

Given how far we are into the season, it is more likely that trends in stats will reflect what a pitcher's final season stat mark will be. Therefore, it is important to consider whether a pitcher has gotten lucky or unlucky overall as you decide whether or not to start him in a potential playoff week. Let's take a look at two BABIP studs and two duds with an emphasis on those pitchers' next starts.



All stats current as of Monday, August 26.

Mike Fiers - Oakland Athletics

(2019 BABIP: .241, career BABIP: .282)

Our first BABIP stud has been a huge surprise this season in terms of his overall success. Veteran Mike Fiers is having his second-straight solid season, going 12-3 with a 3.46 ERA and 1.13 WHIP over 158 2/3 innings pitched. Even more impressive is his .241 BABIP, which is much lower than his .282 career mark. Could Fiers actually be a guy who could help carry teams to a fantasy championship?

Fiers has pitched quite well in August, compiling a 3.00 ERA and 1.08 WHIP in four tough starts against the Cardinals, Astros, and Yankees and at the hot White Sox. However, there are several main things that Fiers has enjoyed outside of his skills that have helped him. The first is his home park. Oakland Coliseum is one of baseball's most pitcher-friendly parks given its deep outfield and massive foul territory, so Fiers has a big advantage in that regard. Further, his recent 4.43 SIERA in that time matches his bloated season-long 5.23 SIERA, which suggests that luck has been on his side in addition to his home field.

Overall, Fiers has pitched quite well but makes me nervous given his perceived actual skill. He doesn't offer high strikeout upside and seems to have gotten by on luck and a favorable park. He does have two starts this week, but I would only be willing to start him at the Royals and not at the Yankees. If you own him and play with weekly rosters, I would only be willing to start him if the rest of your pitchers' matchups are strong.



Kenta Maeda - Los Angeles Dodgers

(2019 BABIP: .253, career BABIP: .283)

Our second BABIP stud has quietly put together another decent season to this point. Kenta Maeda has gone 8-8 with a 4.13 ERA, 1.11 WHIP, and 26.8% strikeout rate in 133 IP. He currently has one of baseball's best BABIPs among qualified starters, but that mark was at .228 last month when I wrote about him. Regression has hit him some already, and while he has still pitched well overall, should fantasy owners be worried?   

Fortunately, most of the positives I found in Maeda's game last month still hold true. The main thing that stands out has been Maeda's amazing batted-ball profile. He has done a great job limiting hard contact (85.2-MPH average exit velocity, 28.8% hard-hit rate, 14.4-degree launch angle), which has helped keep his BABIP down. He also has the benefit of pitching his home games in pitcher-friendly Dodger Stadium. Finally, while Maeda's 4.43 ERA in August has been lackluster, his 3.25 SIERA supports that his batted-ball profile has been legitimate.

In sum, Maeda has still pitched well and is a fantasy asset for the playoffs. He gets a nice two-start slate this week at the Padres and Diamondbacks. I would not be worried as a fantasy owner using Maeda this week or the rest of the season.





All stats current as of Monday, August 26.

Max Fried - Atlanta Braves

(2019 BABIP: .345, career BABIP: .340)

Our first BABIP dud has been a useful fantasy option this season despite posting a very high BABIP. Max Fried has gone 14-4 with a 4.03 ERA, 1.40 WHIP, and 23.7% strikeout rate in 136 1/3 IP this season. He has pitched much better lately, compiling a 3.29 ERA in August, but he is also a young pitcher without a ton of experience. Should fantasy owners feel comfortable relying on him down the stretch?

Fried's August ERA looks good, as does his 3.69 SIERA, but he was inconsistent in those five starts. He allowed two combined runs against the Reds, Twins, and Mets but then allowed eight runs against the Mets and Dodgers. In a way, August was a nice representation of his whole season, showing skill at times but also showing a lack of command. His batted-ball profile has been below-average both in terms of exit velocity and hard-hit rate, so it makes sense that his BABIP would be high.  

I would be a little nervous about Fried down the stretch. It seems like Fried could equally pitch a dud or a gem each week. His next matchup is a deceptively tough one against the White Sox. I would probably start him in a points league but would have to check the standings before starting him in a roto league.



Lance Lynn - Texas Rangers

(2019 BABIP: .332, career BABIP: .306)

Our second BABIP dud has actually been a nice fantasy value this season. Lance Lynn has been solid this season, going 14-9 with a 3.85 ERA, 1.25 WHIP, and 27.4% strikeout rate in 170 2/3 IP. However, his .332 BABIP is currently one of baseball's highest among qualified starters. This has been the case all season, yet Lynn has continued to pitch well. Could this finally catch up to him now?

It is important to note that Lynn has always had higher BABIPs and is now pitching his home games in hitter-friendly Globe Life Park. As such, it is not all that surprising to see his BABIP jump even higher despite him pitching well.

Lynn has indeed pitched quite well this season. His last five starts, however, have been mediocre. His 3.95 ERA  and 28% strikeout rate are ok, but his 1.42 WHIP and 4.40 SIERA are not. The bottom line is that Lynn has an above-average batted-ball profile all season, which has helped him pitch well despite his BABIP. Both his exit velocity and hard-hit rate have been in the top 30 percent of pitchers and he has added strikeouts as well.

Lynn has made things work all season long and has offered more than enough upside to make him worth trusting. He gets a decent matchup against the Mariners at home this week and I would be starting him in that matchup, as well as the rest of his matchups this season.

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Exit Velocity Risers/Fallers - Statcast Hitter Analysis (Week 22)

Exit velocity isn't everything. For example, even though we are looking strictly at data from the past month to identify late-season risers or fallers, Aristides Aquino comes in ranked 250th out of 393 batters on this list with an 87.2 MPH exit velocity. He's done OK for himself despite that shortcoming.

As always, this is a reminder that any individual metric should be taken in context and not used solely as a determining factor for measuring fantasy value. That said, these stats do have value by helping us find players who may be getting hot as the season winds down by making harder contact.

You know the drill - no Trouts, Bellingers, or other obvious names.


Exit Velocity Risers

All stats current as of August 25, 2019 and show results from the past 30 days.


Jake Cave (OF, MIN)

98.2 MPH exit velocity since July 28

Deja vu... In 2018, Cave was a non-factor the entire first half of the season and then jacked 10 homers over the last two months. In September alone, he drove in 21 runs while batting .277. This year, he's on a similar path. Although he's only got three homers and six RBI in August, he is batting an astronomical .452 with five doubles over his last 42 at-bats.

Cave isn't promised daily plate appearances in Minnesota but the fact he's not only scorching the ball but hitting .385 against lefties is a good sign that he'll stay in the lineup for the foreseeable future, making him relevant in 12+ team leagues.


Lane Thomas (OF, STL)

95.1 MPH exit velocity since July 28

Thomas is doing what Tyler O'Neill and Harrison Bader haven't been able to consistently do - get on base. So why has he been sitting, playing primarily as a pinch-hitter, in favor of someone like Bader who is batting .198? Defense. Bader has a great glove and Thomas isn't near his level.

Thomas is slashing a solid .324/.390/.703 with four home runs in 37 at-bats. This should warrant more playing time but for a team leading its division, they are more apt to play it safe and ride the bats of Paul Goldschmidt and Marcell Ozuna. Bader and Dexter Fowler will keep blocking Thomas for playing time, but if Bader keeps struggling or an injury occurs to any outfielder, Thomas could become a priority waiver add.


Ryan McMahon (1B/2B, COL)

93.4 MPH exit velocity since July 28

McMahon has appeared in this space before, thanks to his season-long 91.5 MPH exit velocity that ranks in the 91st percentile, along with a 47.1% hard-hit rate that ranks in the 90th percentile. McMahon has been making solid contact all along. His exit velo has gone up a tick lately but it hasn't really brought different results. His .263 August average is right in line with his .265 season average. The power has increased slightly, with 10 HR post-break compared to seven beforehand. Still, this isn't a sign of a power surge because his launch angle is actually lower over the past two months.

Higher velocity is always good but it needs to be coupled with favorable batted-ball outcomes based on launch angle. Just ask Eric Hosmer.


Exit Velocity Fallers

All stats current as of August 25, 2019 and show results from the past 30 days.


Hanser Alberto (2B/3B, BAL)

81.4 MPH exit velocity since July 28

You probably didn't know his name or his game until a few weeks ago, so let me explain. Alberto is 10th in the majors in batting average despite ranking in the 2nd percentile in exit velocity and is practically dead last in hard-hit rate. He has a Tony Gwynn-like approach that compares closely to Josh Reddick or Cesar Hernandez among modern players. Alberto did hit .330 in Triple-A with the Rangers organization last year but he struggled mightily in his first couple of Major League trials, so it's a shock to see him hit so well. He has an aggressive approach, putting the ball in play 91.2% of the time in the zone. He has walked 3% of the time while striking out 9.1%. Repeated contact, be in weak or strong, will eventually lead to more base hits and it's worked out in his favor so far.

Sadly, Alberto left Sunday's game against the Rays with a bruised head and cervical neck strain. It's not clear whether this will keep him out of action long but mix the injury in with potential regression and a lack of extra-base hits and it could be close to the end of his 15 minutes of fame.


Justin Upton (OF, LAA)

84.3 MPH exit velocity since July 28

It's been a lost season for Upton and not just because he missed two and a half months with turf toe. Upton has struggled to a .213/.310/.404 slash line with 16 XBH over 51 games. His exit velocity is at a career-low (over five years of Statcast data) 87 MPH and his xSLG is down to .380. His strikeout rate is also up while his xBA is down nearly 50 points from his usual mark. All told, Upton has never really gotten in a groove and he's actually getting worse after the All-Star break. It's tough to part with a known commodity like Upton, fearing he could get hot any time, but he's only hurting you at this point.


Kris Bryant (3B/OF, CHC)

85.4 MPH exit velocity since July 28

I think I can officially kiss my KB 4 MVP campaign goodbye. Not that it's been a bad season but .281 with 26 HR and 64 RBI isn't exactly the stuff dreams are made of. He's been scuffling in August with a .238 average despite remaining fairly consistent with his power. Bryant's season-long 87.6 exit velo is up two ticks from last year, back to 2017 levels. It's not near his 2015-16 level of 89 MPH though. At age 27, some like myself thought we could see the best he has to offer now that he is healthy again. We may now want to consider that we already - it came three seasons ago.

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Statcast Slider Usage Studs and Duds for Week 20

Welcome back to RotoBaller’s pitchers advanced stats and StatCast studs and duds article series! Each week we will select an advanced stat, choose two studs and two duds, and analyze what those stats could mean for future fantasy output. Last week I used Statcast's pitch arsenal tab to look at starters' four-seam fastball usage and this week I will do the same for slider usage.

Pitchers' secondary pitches need to be strong in order to achieve high strikeout rates, a metric that matters greatly to fantasy players, so it is worth identifying those starters who can rely on their secondary pitches.

I will pick two high-volume slider pitchers who have done well this season and two who have not or are slumping recently. Let's get into it!


Slider Studs

All stats current as of 8/12/19, courtesy of 


Clayton Kershaw - Los Angeles Dodgers

39.7% slider usage

Our first pitcher needs no introduction; he has been one of baseball's best pitchers for many seasons. Clayton Kershaw has been great once again this season, going 11-2 with a 2.77 ERA, 1.04 WHIP, and 25.4% K rate. While he is known for his looping curveball, Kershaw's main secondary pitch is his slider, which he throws almost as much as his fastball. Let's take a look at this pitch and how it has helped Kershaw.   

Kershaw's slider is so effective thanks in part to its high spin rate (2,674 revolutions per minute). That mark is third-highest among pitchers who have thrown the pitch at least 500 times this season. Kershaw's numbers against the pitch reflect how good it is; his .217 batting average against, .230 expected batting average against, and 18.3% swinging-strike rate are all stellar.

There isn't a ton to analyze here, as most fantasy players know that Kershaw is an elite option. However, it is surprising to see how frequently he relies on his slider. It makes sense as his fastball is not overpowering, but Kershaw is typically known for his curveball (which is also a great pitch). So long as he can stay healthy for the rest of the season, Kershaw will continue to help fantasy teams into the playoffs.



Patrick Corbin - Washington Nationals

36.9% slider usage

Our second pitcher blew up in 2018 because of his nasty slider and has continued to bring it this season. Patrick Corbin relied heavily on his slider last season and some people in the fantasy community were worried that he would not be able to continue to find success using it so much, but he has shown that the pitch really is that dominant. Corbin is 9-5 with a 3.41 ERA, 1.15 WHIP, and 28.6% K rate this season. Let's look at his unusual but effective usage of the slider.

Corbin actually uses the slider as his primary pitch, throwing it more than his sinker (36.9% vs 33.9% usage). The slider is clearly his best pitch; Corbin has a .171 batting average against, .153 expected batting average against, and an insane 26.3% swinging-strike rate. Corbin gets an above-average 2,406 revolutions per minute spin rate on the pitch, causing it to drop about an inch more than the average slider. The pitch is one of baseball's best, which is why Corbin has become a higher-end fantasy option.  

Fantasy players aren't wrong in questioning whether a starter could be successful with a non-fastball primary pitch, but Corbin has shown that it can be done for almost two seasons now. He did scuffle at the start of the season but is locked in now, playing well for a Nationals team that is fighting for a playoff spot. Fantasy owners should feel comfortable relying on Corbin and his slider down the stretch.


Slider Duds

All stats current as of 8/12/19, courtesy of 


Chris Sale - Boston Red Sox

38.3% slider usage

Our third pitcher is in the same top tier as Kershaw but has not performed like it this season. Chris Sale has gone 6-11 with a 4.41 ERA, 1.09 WHIP, and 35.3% K rate. His numbers are excellent other than his ERA, but that won't make fantasy owners feel better. The top concern coming into the season was Sale's health and, while he does appear to be healthy, his fastball velocity has dropped. Consequently, Sale has relied even more on his slider (38.3% usage vs 36.1% fastball usage). There is still time for Sale to potentially help fantasy owners, but what should they expect from him?

Fortunately, all signs point to Sale still being a fantasy stud. His fastball is still quite an effective pitch, but his slider is even better, so it has been a smart transition in using it more. He gets a spin rate of 2,486 revolutions per minute on the pitch, causing it to move a ton (vertical drop of 4.8 inches above league average, horizontal break of 2.9 inches above league average). Consequently, he has a .204 batting average against it, a .172 expected batting average against, and a 17.1% swinging-strike rate.

Further, several signs point to Sale just being unlucky this season. His .312 BABIP is higher than his .293 career mark and his 3.01 SIERA is much lower than his ERA. Overall, Sale still shows all the signs of an elite pitcher, thanks in part to his elite slider.  Unfortunately, he has gotten unlucky, but fantasy owners should not be deterred. given how he has pitched.



Matthew Boyd - Detroit Tigers

37.4% slider usage

Our final pitcher had a huge amount of hype surrounding him in the beginning of the season but has had mixed results since. Matthew Boyd has an impressive 31.9% K rate, but he also has a mediocre 4.16 ERA and 1.17 WHIP. The K rate can be attributed to Boyd's fastball-slider combo, which are pretty much the only two pitches he throws. Does Boyd have what it takes to improve his ERA as well as his K rate?  

Boyd gets a lot of spin on both his fastball (2,390 revolutions per minute) and slider (2,365 revolutions per minute), which has helped him get away with throwing basically two pitches (48.7% fastball usage, 37.4% slider usage). His 19.5% swinging-strike rate on his slider is particularly impressive. Also, like Sale, his 3.34 SIERA is lower than his ERA and his .315 BABIP is higher than his .298 career mark.  

Many signs point to Boyd getting unlucky this season based on his underlying numbers. However, this is the first season that he has found success. I had difficulty trusting him in the beginning of the season based on his history and still feel the same. I could understand fantasy owners relying on him now as they have all season long, but I wouldn't want to leave the fate of my fantasy season on his arm. 


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