In this post I'll begin an analysis of home field advantage in the NFL and its relationship to climate. Others have examined the connection between weather and HFA previously, but here I'll attempt to present the data with a clear and novel approach. This post represents the 'clear' part. In following posts, the novel part will use logistic regression to account for team strength and determine the significance of each particular type of climate match-up.
I began by dividing each home city into four categories: dome, cold, moderate, and warm based on a combination of each city's average December high temperature and wind speed. The dome cities include STL, NO, MIN, DET, IND, and ATL. The cold cities are GB, BUF, CLE, CHI, KC, DEN, NYG, NYJ, NE, PHI, PIT, and CIN. Moderate cities include BAL, WAS, CAR, SEA, OAK, SF, TEN, and DAL. The warm cities are MIA, TB, JAX, HOU, SD, and ARI.
Based on all NFL regular season games from the 2002 through 2006 seasons, the winning percentage of the home team was calculated for each type of climate match-up. I divided the season into "early" and "late." The early season is defined as weeks 1 through 12 and the late season is defined as weeks 13 through 17. For example, the winning percentage of the home team in match-ups of cold teams at moderate cities in the early season is 57%, with n=77 examples of such cases.
The home winning percentage of all types of weather match-ups are presented below in a series of pairs of tables. Each table is presented the same way, with the visiting team climate on the left and the home climate on the top. The first table in each pair is for the early season (pre-December), and the second table is for the late season (December games).
Sample size is usually an issue when populations are divided up among several classes. For that reason, the first pair of tables lists the number of cases of each type. For example, the top right cell of the first table lists the number of games featuring dome teams playing at cold cities in the early season. The same cell in the second table lists the same type of match-up in the late season.
The second pair of tables simply lists the straight-up winning percentage of the home team in each type of match-up. For example, the bottom left cell of the first table lists the home team winning percentage when cold teams play at domes in the early season. The same cell in the second table lists home winning percentage of cold teams at domes in the late season.
What immediately stands out is the very high winning percentage of cold teams hosting dome teams late in the season. The most remarkable result, however, may be the 35% home winning percentage of warm teams hosting moderate teams. Note that there are only about 20 cases of each type of match-up in the past 5 years, so these results could be due to luck or due to general team strengths of the according type of teams. Perhaps a couple moderate teams have been relatively dominant over warm weather division rivals between '02 and '06.
The final table lists the difference in home winning percentage between late season and early season match-ups. Simply put, it is late season winning percentage minus early season winning percentage. A high positive number indicates cold weather may give an advantage to the home team. A negative number or near-zero number suggests otherwise. We'd expect to see a zero for dome teams at dome teams, because outdoor weather is obviously not a factor. This method begins to account for relative team strengths over the period studied.
Take dome teams for example. By reading across, we see that dome teams seem to have no greater HFA in late season than the early season against other dome teams--as we'd expect. We also see that they are at a 20% disadvantage playing at warm cities but, for some reason, have a 9% better advantage playing at moderate cities late rather than early. Lastly, we see that dome teams appear to be at a severe disadvantage playing in cold cities late in the season, apparently giving up 35% advantage to the cold.
As mentioned above, some of the observed differences in HFA due to weather may be due to luck and relative team strengths among the weather-classes. The final table is a simple way of accounting for team strength, but it does not address the possibility that the differences are primarily due to luck. The final part of this article will use logistic regression to more powerfully account for team strength and test for statistical significance.