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.

Our series on how to make sabermetrics more accessible to fantasy owners continues with a closer look at one of the newer Statcast metrics. To check out my previous explanation of Statcast for Pitchers, click here.

Let's begin by identifying what the Expected Metrics are and how they work.

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## 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.

As of January 2019, the Hit Probability formula was modified to include the batter's Statcast Sprint Speed, more accurately representing their ability to beat out a ground ball. 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.

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. It can also be one tool to help you identify if a particular pitcher is getting hit hard or simply getting unlucky, though there are limits to this type of analysis.

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 principle value of all three metrics is to take defense (and therefore actual results) out of the picture, allowing a player to be judged solely on his contact quality (or contact quality allowed in the case of pitchers).

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. Willy Adames of the Tampa Bay Rays had the largest negative differential, with a BA of .278 against an xBA of just .216. A closer look at his profile reveals an unsustainable .378 BABIP, 29.4% K%, and 6-for-11 SB success rate. That's enough red flags to stash him on your "Do Not Draft" list.

Going the other way, Logan Morrison posted the best positive differential with a .238 xBA against a .186 actual mark. Unfortunately for him, he illustrates one of the biggest weaknesses of xBA: it doesn't account for shifts at all. Morrison was shifted in 209 of 226 PAs, a sound strategy considering his 69.6% Pull% on ground balls. It ate him up, limiting him to a .202 batting average overall and just .196 against a "traditional" shift. Positive regression should not be expected in this case no matter what xBA says.

Pitchers illustrate another problem with xBA. Michael Wacha of the Cardinals was the "luckiest" pitcher according to the metric, posting a .278 xBA against an actual mark of .221. However, Wacha didn't pitch in front of random defenders all season: it was always the Cardinals backing him up. The Cards played above average defense in 2018, posting 40 DRS to rank 11th in the league. It stands to reason that above average defensive support would help a pitcher "beat" his xBA, as we have seen in a previous article.

League-wide, major leaguers posted a .248 batting average and .243 xBA in 2018, a five-point differential that continues a trend of declining by exactly one point 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.

## Conclusion

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.

Some fantasy analysts calculate their own expected stats, such as Mike Podhorzer's xHR/FB equation. These metrics are not available on Baseball Savant and require more math than this series is intended to get into. Next time, we'll take a closer look at how to quantify the impact that ballparks have on a player's line.