Bill James in People magazine

People, June 3, 1991.

No kidding.

First (May 31, 1982) up was a review of the then-newly published Baseball Abstract in the "Picks and Pans" section.

Then in 1991 a personal profile entitled "Holy R.b.i.—it's Statman!" (note: capitalization of RBI is in the archive) appeared, and can be found here.


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The Bayes Ball Bookshelf, #2

Baseball Analyst, 1982-1989 (Bill James, publisher and editor)

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SABR is now hosting -- the the blessing of Bill James, and through the work of Phil Birnbaum -- the complete Baseball Analyst.  Between 1982 and 1989, Bill James published 40 issues of Baseball Analyst, which in retrospect is now recognized as the launch pad for some fundamental thinking about using quantitative approaches to understand baseball.

The initial issue got off to a great start, with an article about fielding by Paul Schwarzenbart. In his introduction to the issue, James writes that the article "demonstrates that fielding statistics, like batting and pitching but apparently even more so, are the products in part of circumstances as well as men." This is a topic that, 30 years later, continues to provide plenty of fodder for analysis (e.g. this blog post from a month ago by Tangotiger, "Not all fielding opportunities are created the same").

In later issues, there are articles covering the usual parade of topics: clutch hitting, ballpark effects, how much young pitchers should work, ageing of ball players, and of course movie reviews.

There's also familiar names: Pete Palmer, Phil Birnbaum, and Bill James himself.

All in all, Baseball Analyst is an interesting time capsule. The tools the sabermetric community use to communicate have shifted -- when was the last time you subscribed to a magazine produced on a typewriter and mimeograph? But more importantly, it demonstrates how thinking about these topics has shifted. This shift is both because of further research (we know more than we used to) and because of the proliferation of data and cheap computing power

But it also shows that in spite of 30 years of analysis, there are still many questions unresolved.

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World Series prediction: the Bill James method

Bill James developed a method for predicting playoff series winners, last updated in the 1984 edition of Baseball Abstract in an essay titled "The World Series Prediction System, Revisited".  At that point, it had a pretty good track record -- 73% success in predicting the winner of all the postseason series in the 20th century.

Mike Lynch over at seamheads.com used the method (without any adjustments, updates, or other tweaks) to predict the 2010 World Series -- which correctly identified the Giants.

This year, Lynch has again used the tool and tabulated the Rangers and the Cardinals according to the Bill James method. 

The result:  the Rangers come out as solid favorites.

(A couple of other older references to previous use of method are here and here. Other than that, I haven't found anything on the web that uses or updates the method.)

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Gelman on baseball

Andrew Gelman has published a few blog articles lately that hit on baseball.

First up, "Bill James and the base-rate fallacy", where he points out a flaw in James' reasoning that arises from the "availability heuristic".

Second, at The Statistics Forum, a comparison of predicting future performance at a significant transition point in "Minor-league Stats Predict Major League Performance, Sarah Palin, and Some Differences Between Baseball and Politics".

I don't have anything to add, other than to say it's encouraging to see one of the best statistical thinkers in the academy using baseball as a point of reference.

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Developing talent

Today (Opening Day 2011), an excerpt from Bill James' forthcoming book Solid Fool's Gold: Detours on the Way to Conventional Wisdom appeared on the Slate site. The article is titled "Shakespeare and Verlander: Why are we so good at developing athletes and so lousy at developing writers?", and in it he provides some profound insights into discrimination in sports compared to the rest of society.

But along the way to that point, James takes a shot at the conventional wisdom that expansion dilutes the talent pool. James' contrary view is that expansion creates a short-term dilution, but over the long term more talent develops to fill the increased demand.

The thesis is built on James' assertion that raw talent is abundant, and simply needs the right opportunities -- incentives -- to be developed. In James' thought experiment, an expansion of MLB from 30 teams to 300 would over the long term have no impact on the level of talent, as talent development would expand to ensure the newly available opportunities were filled.

But can we really believe this?  There has been plenty of discussion elsewhere about the distribution of baseball talent (for example, Sabernomics and The Book), all of which would, at first glance, seem to run contrary to Bill James' argument. But those talent curves are drawn based on the current system of incentives, with enough room for 25 roster players on 30 MLB teams and roughly 9,000 players in pro ball in North America and a few more thousand around the world.

Criticisms  of Bill James' essay will no doubt focus on the fact that expanding the number of MLB teams beyond 30 requires some of the non-roster players currently in the minors to move up to The Show ... they aren't good enough to play today, but in an expansion environment they would be.

This might be true in the short term, but as Bill James argues, over the long haul the change in opportunities would shift, and talent would be developed to fill the new opportunities.

Currently around the margins of professional baseball are men who have given up baseball to work as a bartender, and those who have decided to pursue excellence in another sport. Players in both these groups would demonstrate different behaviour when provided a different set of incentives.  The shape of the distribution curve would not change, and the average player's performance would also be unchanged, but the absolute number of players would increase. 

Tom Wilhelmsen, former bartender, now pitching for the Seattle Mariners.

The latter group (the athletically gifted stars in other sports) would provide the increased numbers of players at the top end of the distribution curve, becoming the star players on Teams #31 through #300.  The bartenders of today would become the focus of rigorous development regimes. It's important to remember that not only would there be 10 times more opportunities at very level, but there would also be 10 times more teams trying to succeed, and 10 times more scouts, coaches, and others keen to see their players develop into stars. And this would be repeated around the world, ensuring that the best athletes are active in the sport that provides the greatest opportunities. Given enough time, there would be enough players developed to stock 300 teams with no decrease in overall quality of play.

There are examples of this in the past. One recent example is the growth of information technology occupations -- 40 years ago, very few individuals (both in terms of absolute numbers and as a percentage of the workforce) knew how to write a computer program. But with increased job opportunities and an expansion of training, people who might otherwise chosen other occupations and career paths now can write computer programs. This does not mean the talent pool of computer programmers has been diluted; in fact, an argument could be made that the average talent and the high-end extreme of talent has increased.

Another parallel is the availability of natural resources that lay unused until somebody found a use for it. Petroleum was known to exist for centuries, but wasn't a sought-after resource until the mid-nineteenth century when a method to distill kerosene was developed, making it a cheap alternative to whale oil. In a short period of time opportunities expanded, and as a result there was a rush to develop this previously ignored resource.

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Modeling: insights from the pros

It's been a busy few weeks, so I've spent Super Bowl Sunday* catching up on the various blogs that I try to follow. A couple of posts from Andrew Gelman and Aleks Jakulin caught my eye: Why can't I be more like Bill James, or, The use of default and default-like models and the two-part Model Makers' Hippocratic Oath (Part 1 and Part 2).

All these posts are worth reading in their entirety, but they all boil down to the quote from George E.P. Box: "Essentially, all models are wrong, but some are useful." Knowing (or if you're the author, admitting) the limitations of the model is the most important to understanding how useful a model might be.

*Pitchers and catchers start reporting for spring training one week today!

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Andrew Gelman's "5 Books"

Andrew Gelman is one of the most interesting (IMHO) social scientist/statisticians in The Academy. Not only does he have serious statistical chops (he co-authored Bayesian Data Analysis with Carlin, Stern, and Rubin), but he also has published a raft of papers on voting patterns. His blog Statistical Modeling, Causal Inference, and Social Science -- written with his colleague Aleks Jakulin -- offers wide ranging commentary on everything from statistical theory and philosophy, to R (the statistical software), to all manner of social statistics.
Gelman was recently approached by The Browser to suggest five books on how people vote in the U.S., but instead he provided a list of five excellent books about statistics.  #1 on his list:  Bill James’ Baseball Abstracts 1982-1986.

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Agreeing with Bill James

In 1988, the Bill James Abstract included "A Bill James Primer", with 15 statements expressing what he deemed to be useful knowledge. On that list was:
2. Talent in baseball is not normally distributed. It is a pyramid. For every player who is 10 percent above the average player, there are probably twenty players who are 10 percent below average.


I agree. (Others don't; for further discussion also see here.)


But what is this thing called "talent"? Talent is a combination of a high level of skill and sustained, consistent performance. Skill in baseball is measured through metrics such as ERA (earned run average) and OPS (on-base average plus slugging percentage) -- measures that turn counting stats into an efficiency or rate measure. While this type of measure is important, they fail to account for the fact that some players have lengthy careers, while other players have a very short MLB career. Teams will sign long-term contracts with aging superstars because the player's skill is still above average, even though they may have diminished with age.


In short, career length becomes a valid proxy for talent.


The charts below plot the number of pitchers over the period 1996-2009, by both the number of games played (which favours the relief pitchers) and innings pitched (which favours the starters). During this period a total of 2,134 individuals pitched in MLB -- but the chart shows that very few of them stuck around for any length of time.


At the head of the "games" list at 898 is the still-active Mariano Rivera, while the pitcher with the most innings over this period was Greg Maddux (2887.67 innings; and Maddux threw more than 2,100 innings before 1996, as well). These two individuals, and other Hall of Fame calibre pitchers, are out at the far right of the long tail. Close to the origin at the left are pitchers whose entire career lasted but 1/3 of an inning -- a single out.
Figure 1: Number of Pitchers, by Career Innings Pitched (1996-2009)




Figure 2: Number of Pitchers, by Career Games (1996-2009)


But what of the average skill level of those pitchers? Pitchers who get a small amount of MLB experience (fewer than 27 innings) have a higher ERA than those who get more opportunities to pitch. This group -- 27% of all MLB pitchers -- recorded an average ERA of 8.08, compared to 5.15 for the 42% who pitched between 27 to 269 innings, and 4.45 for the 27% who threw between 270 and 1349 innings. The elite, those who pitched 1350 innings and above, recorded the lowest ERA of all, 4.17.

In spite of the wide variance in the ERAs of the coffee drinkers, the differences in the mean scores are statistically significant.



Figure 3: MLB Pitchers, average ERA, by number of innings pitched (1996-2009)

In summary: there is an abundance of players who are less talented than the major league average, while at the same time the number of above-average talents is low. The distribution, at the major league level, is not normal. Just like Bill James said 22 years ago.

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