World Series Win Probabilities: Batters

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If you looked through yesterday’s graph of every single play from the 2011 World Series thoroughly, you noticed David Freese making a huge impact, as he was responsible for three of the five biggest plays of the series. I mentioned he earned his MVP award; here, we can quantify it. Observe, the leaders and trailers in overall batter series probability added:

Click the image for the full tableau visualization and follow the jump for more analysis.

David Freese’s big performance in the final two games allowed him to run away with the overall series probability lead — at 0.6087, he increased the Cardinals’ odds of winning the World Series by a staggering 60%. Lance Berkman, whose hit off Scott Feldman to tie Game Six was just one of many big hits in the series, checks in at .4507.

To say these two put the Cardinals on their backs would be a criminal understatement — with a +1.06 series probability added combined for Freese and Berkman, the rest of the Cardinals check in at a staggering -0.56. Only Albert Pujols, Allen Craig and Daniel Descalso had a positive impact among the rest of the Cardinals’ hitters. Meanwhile, six Cardinals — Rafael Furcal, Ryan Theriot, Skip Schumaker, Fernando Salas, Lance Lynn, and Jason Motte — notched series probability added marks of -0.10 or worse.

For the Rangers, Josh Hamilton came out as the top hitter with a +.2484 despite carrying a .401 OPS into Game Six. But his valiant efforts in the two deciding games — particularly, the go-ahead home run in the tenth inning of Game Six and the go-ahead double in the first inning of Game Seven — made him the most influential Rangers hitter of the series. His pitchers just couldn’t back it up.

Mike Napoli and Michael Young also put in solid performances for the Rangers, but neither of their best performances came in the ever-important final two games. Another look at the series probability added for all hitters can help us shed some light in exactly which games they helped or hurt their teams, presented below:

Coming up next: a similar treatment for the Cardinals and Rangers pitchers.

Jack Moore's work can be seen at VICE Sports and anywhere else you're willing to pay him to write. Buy his e-book.

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11 years ago

Just out of curiosity, are these Bayesian analysis?

11 years ago

Not at all; their calculated based on observed data at that point in the game. A Bayesian analysis would retroactively estimate the probability added based on what ultimately happened (for at least the inning). Under that approach, Lohse’s bunt would get a ton more credit for moving the runners after knowing Theriot only hit a ground out and Lance Berkman only hit a single in the 10th. In contrast, if Theriot had hit a triple, then Lohse’s moving the runners over had no ultimate contribution.

11 years ago
Reply to  dislikeswpa

That might make for an even more interesting analysis of the game. I wonder, could the prior probabilities be conditioned on the key stat (s) of the pitcher of record at any event of interest?