Deadlines, decisions, and cluster luck

August 1, 2014

Our hometown ball club traded away its ace at the deadline yesterday.  In an attempt to cheer ourselves up we fabricated a sports excuse to discuss data and decision making.  We have done this before…

Back to the Rays:  models predicted they’d win 88 games this year and contend.  What happened?  To provide an answer we start with an earlier post of ours on Moneyball:

No conversation on this topic would be complete without at least a quick reference to perhaps the most popular or ubiquitous example of the Data vs. Intuition debate: baseball’s sabermetrics, a.k.a. Moneyball.  Returning to the McKinsey Quarterly article:

The notion that players could be evaluated by statistical models was not universally accepted. Players, in particular, insisted that performance couldn’t be reduced to figures. Statistics don’t capture the intangibles of the game, they argued, or grasp the subtle qualities that make players great. Of all the critics, none was more outspoken than Joe Morgan, a star player from the 1960s and 1970s. “I don’t think that statistics are what the game is about,” Morgan insisted. “I played the Game. I know what happens out there… Players win games. Not theories.”

Proponents of statistical analysis dismissed Joe Morgan as unwilling to accept the truth, but in fact he wasn’t entirely wrong. Models are useful in predicting things we cannot control, but for players—on the field and in the midst of a game—the reality is different. Players don’t predict performance; they have to achieve it. For that purpose, impartial and dispassionate analysis is insufficient.


Last in cluster luck, first in (some of) our hearts

Next, Exhibit B: an article from three weeks ago in which Grantland assessed the playoff chances of all 30 MLB teams.  Our Rays were playing well but digging out from the huge hole they’d surprisingly dug for themselves before the All-Star break.  The conclusion?  If the season lasted 262 games they’d have time for the bad “cluster luck” to turn around:

(T)he Rays can trace much of their heartache to cluster luck.  I’ve written about hit clustering a couple of times this year, but here’s a quick recap:  Over the course of a week, month, or even an entire season, certain teams’ hitters will bunch their hits together better than others, while certain teams’ pitchers will scatter their hits apart better than others. The Rays have been, by far, the least lucky team in baseball when it comes to hit clustering.  As of Friday’s FiveThirtyEight piece, the Rays had lost a staggering 54 runs simply through poor hit-clustering luck, a full 20 runs worse than the next-unluckiest team, the Astros.  As Peta noted on Twitter, the Rays have turned double plays on less than 5 percent of the baserunners they’ve allowed, an abnormally low number that’s also the worst in baseball.  Some of that is due to defensive slippage, as Ben Zobrist and especially Yunel Escobar are seeing their range start to tail off as they age.  But most of it is likely a giant fluke.

Cluster luck also very likely explains their torrid streak since that article was published.  One of the things baseball fans like to point out about their sport is that the length of the season tends to be a great leveller of performance.  In this case, there is just too little time for the reversion to mean to accomplish a miracle (for Ray’s fans, including some of us.)

This fits well with what we’ve previously written about the role of luck in business, investing, and sports it’s influence is greater than ever because technology and best practices are so widely disseminated and articulated.

The difference between the very best players and the average players is less today than it was in the past.  If skill is more uniform, and luck stays the same, that means luck actually becomes more important in determining outcomes.  It’s everywhere you look.  But one area where you can see it very readily is in sports: In 1941, Ted Williams became Major League Baseball’s last player to hit over .400 in a single season.  Why has no one been able to do that since?  The answer is because skill is much more uniform today.

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