RADIO
With all of the data that is captured for every event in MLB games, there are a lot of fun ways to dive in and see how hitters are truly performing. There is also an opportunity to find what statistical outputs we should generally expect with certain inputs.
For this post, I'd like to revisit some batted ball data and see what those certain inputs (launch angle, launch velocity) usually output (in terms of batting average, slugging percentage, and home run rate).
You can also check out my recent dive into splits for hitters and pitchers by clicking here.
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Batted Ball Type
We have four different "types" to consider: fly balls, line drives, ground balls, and pop-ups. Statcast defines these by certain angle ranges. Those classifications look like this, with certain variations depending on launch speed:
Ground Ball: Less than 0 degrees
Line Drive: Between 0 and 30 degrees
Fly ball: Between 30 and 50 degrees
Pop-up: Above 50 degrees
When we look at each category and check their batting average and slugging percentage it looks like this (2021 data only):
So what you see there is that line drives are best for batting average, and ground balls and pop-ups rarely go for extra-base hits.
Most home runs come from fly balls:
Knowing that you can surmise that line drives are typically singles and doubles, and fly balls are typically either outs, doubles, or homers (there just aren't many triples to talk about).
Launch Angle
Let's break this down even further into individual angles. I went ahead and took the batted ball dataset and rounded each launch angle to its nearest base-five number (so a ball hit at 13 degrees would round to 15, and a ball hit at 21 degrees would round to 20). Here's how batting average and slugging percentage look there:
These curves look very similar, with batting average peaking about five degrees before slugging percentage. If you want hits, you want to be between zero and 30 degrees. If you want extra-base hits, that range shrinks to about 10 to 30. The peak around -60 degrees comes from the infield hits that hit the ground at the point where the catcher can't get to it quick enough and neither can any other fielder in usual situations.
So what can we do with this information in terms of player analysis? Let's get into the data tables!
Finding Batting Average
There are two major predictors of batting average: strikeout rate and line-drive rate. Strikeout rate probably is the best one to look at if you're just going to pick one, but looking at these two together gives you a really good feel for what batting average a hitter should post. Foot speed also helps, as it helps hitters turn ground-balls into hits at a higher rate, but that turns out to be much less powerful in prediction because there just aren't that many infield hits.
So what we can do is find each hitter's K% and LD% and then compare the tandem to the rest of the league. The way I did this here is with the table below. I took the percentile rank for both of those categories (fourth and fifth columns below) and averaged them together (6th column). If you sort by that "Avg Pct" column, you'll see the hitters that are best in both categories.
The leader is Adam Frazier whose 11% strikeout rate and 32% line-drive rate are both near the top of the league. That has resulted in an elite .312 batting average for Frazier. If you flip it the other way around, you see Javier Baez who has a super-high 36% strikeout rate and a low 17% line-drive rate which has resulted in a low .245 batting average (all these numbers are as of August 4th).
Column breakdown:
AVG = actual batting average as of 8/4
K% = actual strikeout rate as of 8/4
LD% = actual line drive rate as of 8/4
K% Pct = Percentile rank of the hitter's strikeout rate
LD% Pct = Percentile rank of the hitter's line-drive rate
Avg Pct = Average of K% Pct and LD% Pct
The names of interest would be the players with low strikeout rates, high line-drive rates, and yet low batting averages. You can see Willie Calhoun stands out here as he's fourth in here but only has a .254 batting average. You would expect that to come up quickly in that case (I know that Calhoun isn't playing right now, but that's just an example of how to best use this table). Some other hitters that should expect more hits to fall in soon: Jose Iglesias, J.P. Crawford, Nolan Arenado, D.J. LeMahieu, Kyle Tucker, Corey Seager, Myles Straw and many others.
On the flip side of that would be the "over-performers", who perform poorly in strikeouts and line drives but still have managed good batting averages. Some names there: Willy Adames, Jazz Chisholm, Shohei Ohtani, Patrick Wisdom, Mitch Haniger, and Jonathan India.
One other thing to note is that exit velocity does not predict batting average. Even at the line-drive level, the average exit velocity makes no noticeable difference. This makes some sense because sometimes a line drive can be hit too hard - which carries it out to an outfielder for an out rather than falling in after it gets over the infield.
Finding Home Runs and Slugging Percentage
The best predictors of home run rates and slugging percentage are fly-ball rate and average fly-ball exit velocity. You can hit a bunch of fly balls, but if you're not hitting them over 95 miles per hour most of them are going to find a glove. Strikeouts are also important here, but not nearly as much as was true with line drives and batting average. Here's the data, presented the same way as above:
Column breakdown
PA/HR = Hitter PA divided by HR, the lower the better
SLG = actual slugging percentage as of 8/4
FB% = actual fly-ball rate as of 8/4
FB Velo = average exit velocity of all hitter fly-balls as of 8/4
FB% Pct = Percentile rank of the hitter's fly-ball rate
FB Velo Pct = Percentile rank of the hitter's average fly-ball exit velocity
Avg Pct = Average of FB% Pct and FB Velo Pct
If you sort by that "Avg Pct" column, you see that Patrick Wisdom comes out on top with his 37% fly-ball rate and average fly ball velocity of 97.5 miles per hour. That has worked its way out to an elite PA/HR at 12.1 (league average is around 27). The usual names are right behind him such as Shohei Ohtani, Kyle Schwarber, Tyler O'Neill, and Joey Gallo. You would expect a very good home run rate from the names in the first few pages of that table after you sort by highest Avg Pct. Some names that show up there without crazy high homer rates: Evan Longoria, Brandon Belt, Miguel Sano, Kyle Seager, Jorge Soler, Justin Upton, and Paul Goldschmidt.
Some of those names have other things going on like playing in big ballparks (Longoria, Belt, pre-trade Soler) or strikeout rates (Sano, Soler, Upton) that will continue to be an issue for them - but it's a good bet to start seeing more long balls from this types of hitters.
On the flip side, it seems that Willians Astudillo, Austin Hedges, Pedro Severino, Manuel Margot, Abraham Almonte, Ty France, Jonah Heim, Ketel Marte, Austin Slater, and some others may have been over-performing a bit in the home run department.
Takeaways
The names I listed above are good guys to look at buying or selling depending on where they showed up. However, the bigger lesson from this analysis is probably just to be mindful of these data. This is most useful early on in the year when batting average and slugging percentages are more subject to randomness - these underlying numbers will tell you ahead of time which hitters are wildly under or over-performing. Keep it locked right here and we'll keep you updated on the numbers!
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