I was, to some extent, inspired by the article by Benjamin Morris on his blog Skeptical Sports, where he suggests that to win playoff games in the NBA, three factors are most important: winning percentage, previous playoff experience, and pace – a measure of possessions. Pace translated into the NFL would be a measure that would count elements such as turnovers and punts. In the NBA, a number of elements such as rebounds + turnovers + steals would factor in.

I’ve recently captured a set of NFL playoff data from 2001 to 2010, which I analyzed by converting those games into a number. If the home team won, the game was assigned a 1. If the visiting team won, the game was assigned a 0. Because of the way the data were organized, the winner of the Super Bowl was always treated as the home team.

I tested a variety of pairs of regular season statistical elements to see which ones correlated best with playoff winning percentage. The test of significance was a logistic regression (see also here), as implemented in the Perl module PDL::Stats.

Two factors emerge rapidly from this kind of analysis. The first is that playoff experience is important. By this we mean that a team has played any kind of playoff game in the previous two seasons. Playoff wins were not significant in my testing, by the way, only the experience of actually being in the playoffs. The second significant parameter was the SRS variable strength of schedule. Differences in SRS were not significant in my testing, but differences in SOS were. Playing tougher competition evidently increases the odds of winning playoff games.

Some notes on the logit. If the probability of a win is P, then the logit of P = ln (P/1-P). Logits aren’t easy to think in, so it helps to have an online logit calculator handy. A logit calculator is here. Using this, you can tell how much effect a single unit change in a variable will have on a logistic formula. If I have a logistic formula that goes logit P = 0.2 + 0.5x_{1} + 0.85x_{2} then I know that a single unit change in x_{2} has an effect on the probability of an outcome that is equal to logit P = 0.85. Using the calculator above, that comes out to about P = 0.7.

The formula is: **logit P = 0.5775 + 0.296 (delta SOS) + 0.765(delta Playoff exp)**. The former has a relative error of about 80%, the latter about 20-25%. The odds these number were obtained by chance is 1% and 3.3% respectively. A logit of 0.765 corresponds to a P of about 0.68 and a logit of 0.296 corresponds to a P of about 0.57.

April 25, 2011 at 3:10 pm

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