While “advanced statistics” are well-ensconced in the baseball world, they are still in fairly nascent stages in the faster-paced worlds of hockey and basketball. For two reasons, baseball is particularly well-suited for this so-called “advanced” analysis: 1) play essentially consists of discrete, one-on-one interactions and 2) a season is long enough to permit the accumulation of a statistically significant number of these interactions, from which meaningful trends can be derived. Hockey lacks both of these characteristics. It’s a fluid sport that rarely features isolated, one-on-one interactions, and numbers people say that the amount of compilable events during an NHL season, which is half as long as a MLB season, are too few to allow for statistical normalization. In other words, the sample size is too small.
Lee Panas’ book on advanced baseball statistics, Beyond Batting Average, which I began reading earlier this year, begins with the deceptively helpful reminder that “[w]ins and losses are indeed what matter.” Statistical data helps to understand why teams won or lost and whether and how they might win or lose in the future.
In the hockey world, advanced statistics, in general, aren’t too advanced just yet, at least when compared with the baseball sabermetric world. At present, the central concept is that, because goals– an obvious leading indicator of success (i.e., wins)– are too rare to be statistically useful, advanced hockey statistics orient themselves around possession. Because it is somewhat difficult, from a practical standpoint, to measure time of possession with useful precision, however, the leading metrics, known as Corsi and Fenwick, simply track those things a player and his team can do only when they possess the puck, which essentially amounts to shooting it.