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Swipe Rate, as its name implies, judges each participant in a base-stealing attempt for his likely effect upon its success. Using a generalized linear mixed model, we simultaneously weight all participants involved in attempted steals against each other, and then determine the likelihood of the base ending up as stolen, as compared to the involvement of a league-average pitcher, catcher, or lead runner, respectively.

(from http://bbp.cx/a/26195)

Stated another way, Swipe Rate allows us to evaluate how good Yadier Molina’s arm is while controlling for the inherent ability of his pitchers to hold runners and the quality of the runners he is facing on base. Likewise, we evaluate the ability of individual pitchers to hold runners while controlling for the possibility that they may be throwing to a catcher with a subpar arm. And for baserunners in particular, we now have something much more accurate to evaluate their base-stealing ability than base-stealing percentage.

Remember that base-stealing percentage, by itself, is not very useful: using straight percentages, an elite base-stealer who swipes 90 percent of his attempts and tries to steal 40 times a year ranks lower than a catcher who had one lucky steal all year (and therefore has a 100 percent base-stealing percentage). In the same way that Controlled Strikes Above Average (CSAA) controls for the effect of other factors on catcher framing, Swipe Rate Above Average regresses baserunners’ steal-success rates against both themselves and others to provide a more accurate assessment of each participant’s effect on the likelihood of a stolen base.

The factors considered by the Swipe Rate are:

• The inning in which the runner was on base;
• The stadium where the game takes place;
• The underlying quality of the pitcher, as measured by Jonathan Judge’s cFIP statistic;
• The pitcher and catcher involved;
• The lead runner involved.

Because the statistic rates pitchers above or below average in preventing stolen bases, average is zero, and pitchers generate either positive (bad) or negative (good) numbers. In 2014, here were the pitchers who were hardest to steal a base on:

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