Moneyballin
/Sports franchises are increasingly being run more like the big businesses they are. In the past, talent evaluation was the exclusive domain of former players and coaches. Now, front offices are being staffed by statisticians. Michael Lewis' Moneyball: The Art of Winning an Unfair Game offers an entertaining and insightful look into the origins and application of advanced baseball metrics in an effort to assess player value.
While the actual game of baseball has evolved with time—baseball gloves weren’t introduced until years later—baseball statistics were mostly stagnant. The baseball box score, which lists both individual and team performance, was conceived in the 1850s by British journalist, Henry Chadwick. Chadwick kept track of the stats he thought were important, which was heavily influenced by what was important in cricket. This led to a hodgepodge of statistics which still cause trouble today.
In the late 1970s, when baseball writer Bill James began self-publishing his annual Baseball Abstracts, a small community of statistically savvy thinkers united. In his books, James would pose questions such as: "If player X only played against team Y, how well would he hit?" He would then try to quantify the different variables, such as stadium dimensions, and attempt to create a model or equation using the available statistics to form an answer. His work wasn’t without its flaws; he wasn’t a statistician, but some of his readership were.
In the years since James first published his Baseball Abstracts, the confluence of increased computing power and wider access to a much larger pool of data has led to a myriad of extremely advanced baseball metrics. The increased availability of data has allowed statisticians to further quantify and model an individual player’s performance.
The baseball narrative of Moneyball, which helped to popularize this more nuanced analysis of baseball, follows the underdog Oakland Athletics’ 2002 season. As general manager for a small market team unable to pay for the best players, Billy Beane looked outside conventional baseball wisdom to remain competitive. He hired a baseball-minded Harvard economics grad who used advanced baseball statistics to model individual player performance. He found various inefficiencies, teams were overvaluing players with certain fundamentally outmoded stats and undervaluing others. Using this different method of evaluating player performance, Beane was able to assemble a team of cast-offs that performed astonishingly well under the circumstances.
What’s even more astonishing—in the age of Big Data and the success of Nate Silver’s journalism—is that there is a culture clash within baseball over advanced statistical analysis. Current and former players as well as baseball columnists occasionally deride “new breed stat guys” of not actually enjoying the game.
DEREK SMITH
Derek is currently enrolled in Algonquin College’s Professional Writing program, is an avid hat-wearer and a voracious reader.
