Archive for stolen base

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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Edit (3/29/17, 7:55 pm EDT): Brent Hershey of BaseballHQ and Ron Shandler’s Baseball Forecaster (very politely) brought to my attention that this has been done before! By Bill Macey back in 2012. Formerly behind a paywall, it has now been made public for your reading pleasure. I didn’t even know this research existed (so I’m really glad Murphy brought it to my attention); I am always reluctant to ever claim to break ground in this field that progresses so quickly but also has such a rich history of research. Please consider the following research a companion to and external validation of Macey’s work.

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I welcome all constructive criticism. This research is not especially rigorous, but given the nature of the claim — a legitimately significant spring training statistic! — it merits the disclaimer.

I found a statistically significant spring training statistic.

I’d rather not rehash the history of research and speculation regarding The Spring Training Stat(s) That Matter. Just know that, outside the modest results from this Dan Rosenheck piece in The Economist, it’s generally accepted that Spring Training statistics mean virtually nothing, and you’ll read all manners of baseball writers bashing this notion.

The big caveat is most of this research concerns individual players. Mine: team-level statistics. Alas, it’s an inherently different beast with which I’m dealing. Despite small within-year populations (30 teams rather than hundreds of players), the observation-level sample sizes are much larger (hundreds of plate appearances rather than dozens), making the odds of finding meaningful correlations much better despite fewer data points.

Per usual, I buried the lede: a team’s rate of stolen base attempts (calculated from stolen bases [SB] plus caught stealing [CS]) during spring training is actually meaningful. I’ll get to the implications of this later because there are many. First, let’s dig into the guts of the research. I gathered team-level spring training statistics from 2006 through 2016 and paired it with regular season statistics from the same span plus 2005.

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Playing time can make or break a baseball player’s fantasy value. An elite player may not finish above replacement level if he suffers an injury and plays only half the season, and a lackluster player could finish above replacement level simply by playing every single day. This is all intuitive, and the fantasy community generally approaches these kinds of things rationally. In other words, most players are appropriately valued, outside of the market inefficiencies that inevitably warp player values.

One-dimensional speedsters — dudes who steal a bunch of bases and do little else — are much harder to peg. Their value is tied up primarily in one category, as stolen bases (SBs) do not directly correlate with other categories the way home runs would with runs and RBI, for example. The issue becomes all the more confounding when one considers the contemporaneous scarcity of SBs relative to home runs. There’s more to value than just SBs and plate appearances (PAs), but the fact of the matter is the two statistics by themselves correlate very strongly with a player’s end-of-season (EOS) value (which, here, are informed by Razzball’s Player Rater).

In the last five years, baseball has seen 75 player-seasons of 30-plus SBs — 15 steals a year on average, a trend that didn’t fundamentally change in 2016 (although that doesn’t mean SBs aren’t scarce). A simple linear regression of SBs and PAs, the latter of which serves as a proxy for other counting stats such as runs and RBI, against EOS value produces a remarkable 0.71 adjusted R²:

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