World Series prediction

The 2011 World Series starts in a couple of days, and it's time for the pundits to come out and make their predictions.  Over on coolstandings.com they've posted their prediction for the World Series.  Here's a screenshot of their "smart" prediction:

(The "dumb" prediction is 50/50 for either team, so there's no point talking about that. And I've posted a screenshot, since their predictions are live and will change upon the outcome of the first game of the World Series. An example of the Monty Hall problem, in real life.)

To summarize:  Texas shows as having a 68.2% probability of winning the World Series.

I'm not sure of the details of their methodology, but we can use each team's regular season win/loss record to employ the "log5" approach to come up with our own prediction.  So I did that, and my first prediction is for a Texas victory (58% probability) -- and if pressed to predict the series length, it would be Texas in 6 games (17% of the outcomes are Texas 4-2).  Both probabilities are substantially lower than the coolstandings prediction.

But we can be a bit more sophisticated in our approach, using an adjusted win/loss percentage that employs a Bayesian adjustment to each team's final result.  (This is the same method I used back in May for the early season results -- after 162 games the impact of the prior is much reduced.) This changes Texas' winning percentage to 0.571, and St. Louis to 0.543.  (Google doc spreadsheet here.)  Using the log5 formula, this gives the Rangers a 0.538 edge over the Cardinals.

Working through the 7 game series, Texas' probability of winning the World Series is 56%.

And we can be still more clever, by considering the road/home splits of each team.

Team      W-L     %   posterior
-------- -----  ----  ---------
Texas    96-66  .593   .571
- home   52-29  .642   .591
- road   44-37  .543   .527


St Louis 90-72  .556   .543
- home   45-36  .556   .535
- road   45-36  .556   .535


The home-road splits improve things for the Cardinals, since they had a better home record than Texas' road record and thus become more likely to win a home game. As well, the Cardinals have home field advantage (but only on game 7 -- the Rangers have home field advantage in a 5-game series. But I digress.)  After using the home-road splits, Texas still remains the favorite, but the probability is down to 54%.

While my approaches still give Texas the biggest likelihood of victory, my estimates are less emphatic than the probabilities over at coolstandings.  Based on the characterizations used at coolstandings, my methods lie somewhere between "dumb" and "smart".  "Average intelligence", perhaps.

Tow Mater says "Rangers in 6. But I had the Phillies and the Brewers beating the Cardinals, too".

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And on the topic of infographics...

 Source:  http://www.flickr.com/photos/smoy/6143338263/, via Andrew Gelman.

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WPA contribution infographic

I like these WPA word cloud graphics from SB Nation by Kevin Dame, describing the player contributions in last night's Tiger-Yankee ALDS game 5.  (From
http://mlb.sbnation.com/2011/10/7/2474640/yankees-tigers-game-5-visual-box-score.)

One of the things I like is that they emphasize that that WPA (Win Probability Added) is circumstantial. 

For the Tiger pitching staff, the starter Fister gave up only one run over five innings (that is, four scoreless innnings), but gets a smaller font than the closer Valverde who worked only the scoreless ninth inning.  An easy example is Fister worked a 1-2-3 1st innning with a 2-0 lead, which was worth 0.052 WPA.  By contrast, Valverde's 1-2-3 9th inning with a one-run lead (3-2 score), was worth 0.222 WPA.  Being later in the game and with a tighter score yielded a higher WPA.

And for the Yankee hitters, ARod's strikeout to end the game (the end of Valverde's 1-2-3 ninth) was only one-third as important to the Yankee defeat (-0.053 WPA) as Swisher's strikeout to end the 7th inning, when the bases were loaded (-0.154).  Of course, on Swisher's strikeout Tiger pitcher Joaquin Benoit set himself up for the big 0.154 WPA by coming in with a runner on 1st, then giving up two singles to load the bases, followed by a walk to close the lead to one run.

(The Fangraphs box score has the details that were used to make the word clouds, while the individual play log, with the WPA for each at-bat, is here.)

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Actuarial baseball

Been off the grid for a while...

A couple of weeks ago, Josh Hamilton of the Rangers hit a grand slam that got an above-average amount of attention, since it was tied into a promotion being run by a flooring company.  The title of this article describing the homer could instead be "Josh Hamilton's grand slam yields big insurance payout":
http://espn.go.com/dallas/mlb/story/_/id/6971469/texas-rangers-josh-hamilton-slam-triggers-free-flooring-payout

Somebody, somewhere, in some insurance company, sold coverage for this promotion.  And that same somebody (we hope) must have sat down and calculated the probability of Hamilton hitting a grand slam over a one month period, and set the premium based on that probability.

Summary:  insurance is gambling.

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Bayes book

I recently learned about a new book by Sharon Bertsch McGrayne, The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy.

I haven't got a copy yet, but based on a couple of reviews, it seems like it's going to be a good read. The review in Significance magazine describes it thus: "At times reading like a historical account, at times like investigative journalism, at yet other times like a statistical commentary."

Other reviews:  New York Times Sunday Book Review, Boston Globe, and Nature (subscription required for on-line access).

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Physics of baseball

In 1990, I read a great little book called The Physics of Baseball by Robin K. Adair (now in its third edition).  It's strongly recommended to anyone interested in the subject matter.  And here's a short Q&A with Adair at Popular Mechanics from a couple of years ago.

But a few other items of note on this subject have popped up recently.

First, a great chart at The Book, with the speed of the ball off the bat as the X axis and the angle of launch as Y, showing the outcome (from ground ball to home run) at the points X,Y.

Then, there's an article in the latest issue of the American Journal of Physics by Faber, Smith, Nathan, and Russell called "Corked bats, juiced balls, and humidors: The physics of cheating".  You can also find a short summary of the article at Smithsonian.com. 

There are three questions asked, each with a nuanced answer.

1. Question: "Can a baseball be hit farther with a corked bat?"
Answer: "... while corking may not allow a batter to hit the ball farther, it may well allow a batter to hit the ball solidly more often."

2. Question: "Is the baseball juiced?"
Answer: The researchers "found no evidence that baseballs of today are more or less lively than baseballs used in the late 1970s."

3. Question: "What's the deal with the humidor?" (Or, "is it plausible that the humidor accounts for the decrease in offensive statistics at Coors Field since 2002?"
Answer: Yes.

For those interested in a deeper dive into this topic, one of the co-authors of the study, Alan Nathan, has a page dedicated to the physics of baseball.


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Update 2011-07-13:  Tango at The Book posted a link to the Smithsonian article, and there has been plenty of commentary including a number of responses from Alan Nathan.

Two months in, a Bayesian look at the standings

At the end of April, I posted "Early season standings and Bayes" that took two different approaches to regressing the early season standings to come up with a prediction for the eventual result for the full season.

So here we are at the end of May, and there's been a lot of movement in the standings, so here's an update to the spreadsheet. Although the Phillies and the Indians remain at the top of the standings, they are starting to regress downwards. At the end of April, both teams were "on pace" to win 112 games in the season, but the regression showed a more modest result of 93 wins. A month later both teams are "on pace" to win 100, but the Bayesian approach suggests that they are more likely to win 91 games.

If you are a Twins or an Astros fan, there is no solace in the fact that both teams have not regressed toward the mean over the past month, but have instead continued to play at roughly the same level they exhibited in April. The regression model now predicts the Twins will end up at 65 wins, which would be the lowest in MLB. Of course, this prediction is based only on the team's performance to date -- it doesn't consider the number of injuries the Twins are currently dealing with.

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