ABSTRACT
In The Numbers Game, Alan Schwartz details the history of player performance measures in professional baseball. Soon after players took the field for professional teams in the 19th century people began to track such measures as hits and at-bats. And in 1870, H.A. Dobson noted that a baseball player’s efficiency could be measured simply with batting average — calculated simply by dividing hits by at-bats. As time went by increasingly sophisticated metrics were developed, such as on-
base percentage, slugging percentage, OPS (on-base percentage plus slugging average), among others. Tracking the statistics and developing new metrics does require the expenditure of some effort. Is there a benefit generated to offset this cost? The answer to this query begins with why player statistics are tracked in the first
place. For any game we can see which team won and which team lost by looking at the scoreboard. The question we have is which players on each team were primarily responsible for the outcome we observe. To get at this question, interested observers, i.e., teams, the media, fans, analyze
player statistics. The purpose behind this effort is to separate each player from his team and connect that specific player’s actions accurately to the team outcome observed. There are two reasons why we wish to separate the player from his team. First, we
wish to explain why a specific team has won or lost. Specifically, which players are
most responsible for the outcome we observed? From the team’s perspective, though, there is a more important issue. Teams need to know which players to employ in the future. By tracking and analyzing player statistics teams hope to identify the players who will help the team be successful in the future. In sum, statistics are tracked to both explain what we observed in the past and determine what actions a team should take in the future. In baseball the first task is relatively easy. The statistics tracked for baseball play-
ers — singles, doubles, triples, home runs, etc. — are clearly linked to runs scored and wins. And understanding the value of these statistics is fairly easy. One does not need advanced regression analysis to understand that one more home run is worth more to a team than one more single. As noted in Berri, Schmidt, and Brook (2006), baseball performance is not entirely
consistent across time. So forecasting future performance in baseball is somewhat difficult, even if one completely understands the data collected. Basketball also has an abundance of player statistics. Teams track for each player
points, rebounds, steals, assists, and other factors to help decision-makers both explain and predict performance. Relative to baseball, the explanation of why teams win and lose is a bit more difficult in basketball. After all, which is more important, an additional point scored or an assist? Would a team rather have one more rebound or one more blocked shot? As noted in Berri et al. (2006), with a bit of thought one can untangle the relative
value of these statistics. Furthermore, relative to baseball, players in basketball tend to be more consistent across time. Hence, player forecasts are relatively more reliable in hoops. What about the American football? Yes, a number of statistics are tracked. But
statistics are only commonly tracked for certain positions. Quarterbacks and running backs each have a number of factors tracked and these clearly can be used to assess performance. Other positions, though, such as offensive lineman, have hardly any statistics. Still, numbers do exist in football. The purpose of this essay is to explore how these statistics can be used to explain past performance, as well as predict the future. In other words, do statistics in the National Football League (NFL) have the same value as statistics tracked in baseball and basketball?
