Fall is a special time of year for North American professional sports. Autumn gives us the MLB playoffs, the start of the NHL and NBA seasons, as well as the heart of the NFL season. It’s a terrific time to be a fan (particularly if your favorite team happens to have a cyborg named Madison Bumgarner).
While the fall gives us a buffet of sports viewing to choose from, the juxtaposition of the analysis available for each sport has grabbed my attention recently. Why does Baseball have an endless set of statistics for players and teams? If each NFL snap has 22 actors, why does each play have only a small handful of data points recorded? Why is it difficult to tell the extent to which NHL MVPPatrick Kane’s stats were inflated because teammateJonathan Toews’s line drew top defensive pairings? Why haven’t we seen aBilly Beane,Bill James, orNate Silverin the NBA?
The answer isn’t obvious at first, but it comes down to the simple concept of measurability, which is broadly affected by a) how discrete the objectives are, b) what percentage of play is “on-the-ball”, and c) how important individual contributions are. In Baseball, the most data-driven sport, we find the perfect storm for analytical analysis. The objectives are nearly always consistent (offense avoids outs, defense pursues outs), all action happens on-the-ball (the defense can’t make a play without it), and the crux of the game is 1v1 (pitcher vs batter). That structure yields a game where nearly everything is measurable, with the challenging last frontier of individual defensive statistics quickly being addressed as teams seek competitive advantages.
Baseball stands in sharp contrast to American football. In football, the objectives vary for both offense and defense based on down, distance, score, and time. On-the-ball success may be attributed to a quarterback or running back, but that success is built on the off-the-ball work of 10 other teammates whose contributions are scarcely recorded. Pittsburgh Steelers running back Le’veon Bell may rush for a 100-yard game, but it’s difficult to assign fractions of that success across teammates and coaches.
The ambiguity of American football statistics has upside for the smartest teams – over the past 20 years, just 6 teams have accounted for 75% of championships. Clearly, better data analysis in player acquisition as well as on-field strategy can pay sustainable dividends when data analysis is complex. In contrast, absent a hard salary cap, the end-game of the baseball data revolution may be an efficient market where smart big-market teams outspend smart small-market teams, with the only noise being injuries and luck. For proof, look no further thanTheo Epstein, former GM of the Boston Red Sox, currently GM of the NL favorite Chicago Cubs.
When I look at the broader world of business data, I see a quite a few parallels to the sports world and the ability to derive competitive advantages.
