How Sprint Speed Relates to Stolen Bases

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:

  • SB%
  • SBO per plate appearance (SBO/PA)
  • Player SB attempt (SBA) per SBO (pSBA/SBO)
  • Team SBA per SBO (tSBA/SBO)
  • Speed score (Spd)
  • Sprint speed

Rather than boring you with theory like I did yesterday, I’ll cut to the chase. I produced a correlation matrix, which depicts the Pearson r coefficients (where +1 means perfectly positively correlation, -1 means perfectly negatively correlated, and 0 means no correlation at all) among the aforementioned metrics. Cells highlighted yellow (orange?) denote meaningful correlations:

Stolen Base Correlation Matrix
SB% SBO/PA pSBA/SBO tSBA/SBO Sprint speed
SB% 1.000
SBO/PA -0.010 1.000
pSBA/SBO 0.320 -0.106 1.000
tSBA/SBO 0.158 -0.053 0.294 1.000
Sprint speed 0.298 0.062 0.598 0.054 1.000
SOURCE: Baseball Reference, Baseball Savant/Statcast

Let’s break down each one.

  • SB% and pSBA/SBO: A baserunner’s success rate correlates weakly with how often he runs. This makes some sense; the players most likely to run are probably those most likely to succeed.
  • SB% and sprint speed: A baserunner’s success rate correlates weakly with how fast he can run, at peak speed. Indeed, not everyone who can run fast should run fast. Moreover, there’s a caveat in here about small sample sizes, which I’ll make shortly.
  • pSBA/SBO and tSBA/SBO: A baserunner’s frequency of stolen base attempts correlates weakly with his team’s frequency of stolen base attempts. This result is actually pretty cool. It’s not anywhere close to the be-all and end-all of the discussion, but it does demonstrate that team context does make a non-zero impact on a player’s number of attempts.
  • pSBA/SBO and tSBA/SBO: A baserunner’s frequency of stolen base attempts correlates moderately with his sprint speed. Again, also a pretty cool result. The faster the runner, the more likely he is to try to steal a base. In a sense, a player’s frequency of attempts is more stable than his frequency of successes.

Getting to the sample size caveat: stolen base success rate is a fool’s errand to predict, at least within a single season. We concern ourselves so greatly with sample size throughout the season yet readily overlook an outrageously small sample size when it comes to stolen bases. The stolen base attempts happen throughout an entire season, which makes the sample feel big, temporally speaking. But is 30 stolen base attempts a lot? Not by any stretch of the imagination. Remember when Byron Buxton went 29-for-30 on stolen bases in 2017, and the community mistook it for acute baserunning acumen rather than an historical outlier? To be clear, at 46-for-51 (90 percent) in his big-league career, it’s apparent Buxton is an excellent base-stealer. But even he can’t sustain a 97-percent clip — and a lot of that is simply the function of the volatility in small samples.

Like yesterday’s post, today’s research didn’t do much beyond confirm some central tenets of baseball’s common assumptions. Guys run more when their teams run more. Guys who run more succeed more. Fast guys run more and they succeed more, but at a smaller magnitude.

If there’s any takeaway here, it’s that if you’re one who calculates his or her own stolen base projections, you’re best off relying on multiple years’ worth of prior statistics, not just the previous year.





Two-time FSWA award winner, including 2018 Baseball Writer of the Year, and 8-time award finalist. Featured in Lindy's magazine (2018, 2019), Rotowire magazine (2021), and Baseball Prospectus (2022, 2023). Biased toward a nicely rolled baseball pant.

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FrodoBeck
5 years ago

1. Identify teams that like to steal bases.
2. Pick players from those specific teams that are fast.
3. ???
4. Profit