My fantasy baseball draft was last night. Our league—which, incidentally, was started by a few baseball-starved Americans in Oxford about 15 years ago—is a “keeper league,” meaning you retain players from the previous season’s roster. I kept Blue Jays first-baseman Vladimir Guerrero Jr. and Rays shortstop Wander Franco. Once “keepers” have been selected, it’s a draft, which entails combing through statistics for various MLB (and MiLB) players and trying to determine who will prove valuable over the coming season. The process, particularly if you want to win, can be quite complicated. Deciding on which players to draft requires that one learn to identify the most meaningful player data and statistics and, even more crucially, to interpret them well. Take Royals catcher Salvador Perez. He had a career-year in 2021, swatting 48 home runs and tallying 121 RBI. But a glance through his profile on Fangraphs reveals that last season was anomalous for him in a number of ways. Discounting the 2020 season, which was shortened due to the COVID-19 pandemic, Perez posted his best walk rate (4.2%, which is by no means elite) and batting-average-on-balls-in-play (.298) in nearly a decade. As a catcher entering his age-32 season, it seems unlikely (if not impossible) that Perez will be able to extend his remarkable 2021 season into 2022. So I passed on him and opted for Dodgers backstop Will Smith instead. Time will tell if I made the right call!
To be sure, one of the best things about baseball is the game’s capacity for debate and judgment. Coaches, players, scouts, and fans are constantly trying to find new ways to evaluate talent and performance. At first glance, it might seem that quantitative metrics clear up most of these questions. But this is true only up to a point. After all, someone has to evaluate those metrics, and human beings are partial, fallible, and thus liable to make mistakes. This was an issue explored in Moneyball (2003), Michael Lewis’ classic account of how the Oakland A’s gained a competitive advantage (at least for a time) by finding value in oft-neglected statistics (such as On-base Percentage) and heretofore overlooked players (such as journeyman catcher/first basemen Scott Hatteberg). Lewis’ book was later made into an equally excellent 2011 movie. In this scene, A’s general manager Billy Beane (Brad Pitt) and his assistant Peter Brand (Jonah Hill) discuss this new approach to player analysis:
What’s interesting here is that many of the players in question—relief pitcher Chad Bradford, for example—were already statistically successful players. It’s just that MLB front offices failed to see it. They confused that which really matters in baseball (recording outs, getting on base) with that which is accidental and aesthetic. Bradford thrived at getting batters out, but he didn’t throw hard enough or use a traditional throwing motion. The numbers “worked,” but the people “read” them wrong, biased by cultural and/or personal preferences.
With this in mind, it is not surprising that baseball writer and statistician Bill James once concluded that induction into the Baseball Hall of Fame is a matter of human judgment, rather than one of statistical “proof.” For, in the end, even objective measures have to be subjectively interpreted, and thus the essential task is to learn to interpret well, to understand the numbers that matter and the ones that don’t, the ones that reveal the quality of a good player and the ones that can obscure the shortcomings of a weaker player. (Famously, James and his disciples dismiss batting average as a meaningful statistic). As James once put it:
There is no way in the world to evolve a set of standards which is as comprehensive, as complex, as fair, or as open to improvement as is human judgment. I have spent all of my life…learning to understand baseball records. If I couldn’t make up standards which are fair and comprehensive, who could? And I don’t feel that I could. There are simply too many things in the game of baseball which are not measured, are poorly measured, are still in the process of being measured.
Indeed, this is a notable “postmodern move” by James, who otherwise would seem to represent the modernist tendency to reduce meaning to a putatively disinterested datum. Moreover, James’ arguments parallel a number of recent responses to the rejection of religion by scientists such as Richard Dawkins. Indeed, according to Alister McGrath, the problem with Dawkins is not that he champions science but that he fails to realize that science too is a historically-conditioned discipline that depends on sensitive and sound human judgment.
Maybe I should’ve asked Dawkins to join our fantasy league when I had the chance?