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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 manager is interested in.
With that out of the way, 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. 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. His batting average indeed regressed to .277 last year, though his .296 xBA suggests that he deserved a better fate. He's actually getting better, much to the consternation of pitchers everywhere.
Going the other way, Marcell Ozuna posted the best positive differential with a .288 xBA against a .241 actual mark in 2019. 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 looked like a nice bounceback candidate. He was exactly that, hitting .338 with a .312 xBA last season.
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 outstanding glovework like Francisco Lindor's 11 Outs Above Average could help sustain such a gap moving forward. Plesac improved in several areas last season, though Cleveland's defense appeared to help him again (.191 BAA vs. .229 xBA).
League-wide, major leaguers posted a .252 batting average and .250 xBA in 2019, a two-point differential that has declined in each full 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. Check out this link to brush up on other metrics you can use in your fantasy draft prep.
