Fantasy analysts and enthusiasts alike are still searching for ways to use Statcast’s expected wOBA (xwOBA) metric meaningfully to gain an edge. Unfortunately, beyond leveraging the difference between xwOBA and actual wOBA (what I, and probably countless others, refer to as the “wOBA minus xwOBA differential”), I don’t know yet how else you can use xwOBA effectively. Given already-widespread use of the metric, the minimal edge you can glean will come from interpretation.
I discussed the interpretation of xwOBA multiple times in 2018. In May, I highlighted hitters on whom to buy low because of their extreme/outlier wOBA–xwOBA differentials. In July, I called out xwOBA’s inability to account for what appeared to be the ball becoming un-juiced, thereby overestimating xwOBA across the league. In September, I investigated the predictiveness of xwOBA in-season (that is, the predictiveness of first-half xwOBA on second-half wOBA).
I discussed all of these topics during my presentation at BaseballHQ’s annual First Pitch Arizona forum, especially the former-most. Basically all of the hitters I tabbed as buy-lows outgained their prior performance by substantial margins — all of them, that is, except for Victor Martinez. Could he be considered a miss? Sure, except he was different from the rest of his fellow underachievers: he perennially underperforms his xwOBA. Perhaps the better question, then, is: Why was he a miss?
Answer: foot speed. It’s no secret xwOBA doesn’t account for a player’s speed in its calculations. I’m being literal; the glossary entry for xwOBA explicitly states it only accounts for launch angle and exit velocity on batted balls. As I’ve speculated in the previously linked posts, foot speed appears to be the common thread among xwOBA’s biggest over- and under-performers (by measure of wOBA–xwOBA). Maybe I’ve buried the lede, but I’m here to validate this suspicion once and for all. I mean, it’s hardly a suspicion — it’s hiding in plain sight — but I have yet to see anyone publicly confirm it. So, here I am.
I called upon the following 2018 data sets, all pulled from Baseball Savant:
- wOBA–xwOBA on ground balls, min. 150 PA (n=246)
- BA–xBA* on ground balls, min. 100 PA (n=246)
*Batting average minus expected batting average
- wOBA–xwOBA on all batted balls, min. 400 PA (n=213)
- Sprint speed leaderboard
I wanted to look specifically at over- and under-performance on ground balls because these seemed the likeliest scenarios in which foot speed would make a huge difference. While faster players can stretch select singles (and doubles) into doubles (and triples), the biggest difference would be seen on infield ground balls, turning outs into hits (or vice versa). Sprint speed captures a player’s fastest running speed over any one-second interval. Thus, while it does not capture his most likely or even his average running speed, it does capture his speed while busting ass to first base. It’s important to note that some hitters might never bust ass to first base in a season (Albert Pujols, for example), but that doesn’t undermine the theory here.
So, can xwOBA discrepancies be attributed, at least in part, to different sprint speeds? I calculated the Pearson r correlation coefficients (where +1 means perfectly positively correlation, -1 means perfectly negatively correlated, and 0 means no correlation at all) for sprint speed with wOBA–xwOBA and BA–xBA on ground balls:
- wOBA–xwOBA: r = +0.61
- BA–xBA: r = +0.59
Depending on your interpretation, those relationships are anywhere from moderate to strong. In other words, the answer is a resounding yes: sprint speed matters. It’s why the fastest runners (e.g., speedy center fielders) routinely post positive wOBA–xwOBA differentials while the slowest runners (e.g., lumbering first basemen) routinely post negative wOBA–xwOBA differentials.
On ground balls, each additional foot-per-second of sprint speed above break-even (roughly 26.4 ft/sec) corresponds with a 20-point increase in wOBA–xwOBA and a 21-point increase in BA–xBA, all else constant (meaning, when comparing two players, they’re identical in every way except for sprint speed). We can expect Trea Turner’s sprint speed of 30.1 ft/sec to produce, specifically on grounders, a BA–xBA of +0.076 (in actuality, it was +0.057) and a wOBA–xwOBA of +0.072 (it was +0.048). The all else constant caveat here is important — there’s more at play here than simply sprint speed, so we can’t declare Turner lucky or unlucky be measure of wOBA–xwOBA based on sprint speed alone.
I also measured the correlation between sprint speed and overall wOBA–xwOBA:
- wOBA–xwOBA: r = 0.47
It’s lower, but I’m not surprised. I guessed that sprint speed would have the largest impact on ground balls. It plays no role in pop-ups and virtually no role in routine fly balls, so once you’ve incorporated the entire batted ball profile, the overall effect (which is concentrated on ground balls) diminishes.
It’s possible that this post did nothing but confirm your suspicions. Likewise, friend. The result here is not remotely surprising (to me, at least), but now it’s quantified in some capacity. More rigor and intellectual digging could unearth other nuances in the relationship between sprint speed and the wOBA–xwOBA differential. Until then, this snapshot into how extreme sprint speeds adversely affect xwOBA’s inability to adequately measure ground ball value.
Two final caveats. First: this analysis concerns 2018 data only. Given year-over-year differences in how xwOBA measures performance — as captured in detail here — it’s likely the aforementioned relationships with sprint speed fluctuate each year, albeit only slightly.
Second: apparently MGL (via Tom Tango) looked at the effect of sprint speed on a player’s wOBA projections. He calculated changes in players’ sprint speeds year over year and investigated whether those changes led to wOBA projections being over- or under-estimated in the following year. (For example, would a change in sprint speed from 2016 to 2017 portend a wOBA projection discrepancy in 2018?) Whereas I used sprint speed to demonstrate the descriptive shortcomings of xwOBA on wOBA, MGL showed the predictive effects of sprint speed on wOBA projections, exclusive of xwOBA. The research is fundamentally different, but the results are generally the same. Whether within years (e.g., 2018 only) or across years (e.g., 2016 through 2018), there appears to be shortcomings with both describing and predicting wOBA when not accounting for both static levels of and changes to sprint speed.