Archive for sprint speed

In rounding out my series on the most important factors influencing components of BABIP, I will be looking into what most affects a hitter’s batting average on ground balls. So far, the results of these analyses have been consistent. Launch angle has been the main driver of BABIP for pitchers, and it has been for hitters as well, at least when they are launching flyballs. The story is a different one, though, when hitters put the ball on the ground. Ground ball launch angle was not a significant factor in determining a hitter’s ground ball batting average, and neither was ground ball exit velocity.

Whether or not a hitter pulls grounders has much to say about whether that player will hit for average on grounders. There is a negative relationship between these variables that is significant at p < .0001 and with a Pearson’s r of .27. Even more important is how fast the hitter is. The Pearson’s r for the positive correlation between average sprint speed and ground ball batting average was .45.
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Yesterday, I wrote about how sprint speed relates to wOBA minus expected wOBA (wOBA–xwOBA). Today, I summarize my investigation into what factors most readily affect a player’s stolen base success rate (SB%).

This invitation from BatFlip Crazy, embedded in this lengthy Twitter exchange, served as the catalyst for the research. In hindsight, I’m not sure I totally answered the question. Manipulating data from multiple different sources (in this case, Baseball Reference and Baseball Savant) can be exhausting.

I used my final Frankenstein data set, which contained statistics for all players from 2016-18 with at least 100 stolen base opportunities (SBOs) in a given season, to investigate relationships among the following various stolen base metrics:

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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?

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