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Moneyball

If you’re wondering where I got my love for sports analytics, the answer is Moneyball. Published in 2003, Moneyball explains Sabermetrics through the story of Oakland Athletics General Manager Billy Beane. As defined by Wikipedia, Sabermetrics is the empirical analysis of baseball, especially baseball statistics that measure in-game activity“.

Moneyball generated a lot of controversy. These stats nerds were ruining the game with complicated algorithms. At the time, Beane thought players who excelled in on-base percentage were undervalued compared to those with a high batting average. Old school analysts like Joe Morgan mocked those who valued on-base percentage for wanting to clog up the basepaths.

The Oakland Athletics haven’t come within a 10-foot pole of a World Series. Why is Moneyball so lauded?

I evaluated whether on-base percentage is a better statistic than batting average. If you think that’s a poor way to evaluate the value of Sabermetrics, I agree. But it is probably the most popularized insight from Moneyball and makes for a simple test.

Using MLB data from 2003 to 2018, I calculated a multiple linear regression between batting average, on-base percentage, and wins. Here was the result:

Wins = (OBP * 560) – (BA * 204) – 49

This means to estimate how many games a team will win, you multiply their on-base percentage by 560, subtract their batting average multiplied by 204, and subtract 49. After controlling for OBP, having a higher BA is associated with a smaller chance of winning.

How could this be? Hits make you less likely to win? Not exactly. On-base percentage includes hits, walks, and hits by pitch. Since batting average includes hits, on-base percentage captures everything captured by batting average and then some.

To confirm this, I calculated two simple linear regressions, that between on-base percentage & winning and that between batting average & winning.

Wins = (OBP * 409) – 52
Mean Squared Error = 98

Wins = (BA * 340) – 7
Mean Squared Error = 113

First, we see the coefficient is stronger for on-base percentage than for batting average, 409 compared to 340. Second, we see the mean squared error is smaller for on-base percentage meaning the relationship is clearer. Conceptually, on-base percentage is more valuable to me than batting average. But if you’re skeptical and wanted the data, there it is.

The debate between on-base percentage and batting average is only one example between the statistics nerds and the old guard. Over a decade after Moneyball was published, on-base percentage is now properly valued. The promise of using statistics and economics to recognize undervalued assets in sports remains as alluring as ever.

Thanks to Kyle Safran for helping me prepare this post.

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Sports Journalism is Terrible

Sports journalism is awful. Simply dreadful. It is full of cliches, empty rhetoric, and utter nonsense.

While at the gym today, I saw a segment on Sportscenter (the premier sports journalism show) about the firing of Seattle Mariners manager Lloyd McClendon. In the segement, one of ESPN’s top baseball experts Tim Kurkjian said more General Managers are willing to fire Managers today because the relationship between GM and Manager is more important than ever before. Kurkjian was asked a simple yet (would be) illuminating question: why is this? That is, what has changed to make this relationship more important.

Kurkjian gave a classic non-answer, offering up “the GM and Manager need to be on the same page” for five minutes, which is essentially repeating the question. An absolute garbage answer. I can’t say I’m too surprised. Kurkjian didn’t present any basic statistics about the rate of manager firings in the MLB over time so I have no reason to believe his claim is even true. If I had to guess, if pressed, Kurkjian would admit that deep down he doesn’t really know either.

My inner economist speculates the incentives for good or poor sports commentary are too weak. Sports journalists aren’t forced to revisit their predictions (that means you, John Kruk). I’m a big fan of prediction markets because of the power of price signals to share information. I wonder how a sports analysis website centered around a prediction market would fare. It should provide more accurate insights and predictions, which would lead to greater viewership if other sports fans are looking for that.

My favorite criticism of sports journalism came from the blog FireJoeMorgan.com. In over 1,000 posts over 3 and a half years, the bloggers pointed out the absurdities of baseball “experts”. The blog was named for Joe Morgan, who was one of the most high-profile commentators who fit their profile, but addressed many other sports pundits (Kurkjian included).

The blog ran from 2005 to 2008 in which Michael Lewis’ Moneyball was topic of debate within professional baseball. There was a common thread between the sportswriters profiled by FJM: they criticized Moneyball without having a rudimentary sense of what it was really about (Morgan even showed his ignorance regarding Oakland Athletics GM Billy Beane’s role in the book: he is the subject matter, not the author). While they did not identify themselves at the time, Michael Schur and Alan Yang (who went on to create Parks and Recreation) were contributors to the blog.