Monday, May 08, 2006
I've watched a lot of NBA games in my life, and I'm always alarmed at the calls made by referees that seem to favor the home team. I think I really started to notice it a few years ago in the Western Conference Finals between the LA Lakers and Sacramento Kings--I think the Kings would have won the series if the officiating had been fairer. Complaints with NBA referees go far further back than this series, and have a rich history. It has often been argued that Jordan "got all the calls" because of his accomplishments. I want to test if the home team does, in fact, get more favorable calls.
What's more, I've always believed that these calls occur during pivitol times of the game. NBA referees are social creatures like anyone else, and I believe that they are influenced by the crowd and the time of the game. I hypothesize that referees in better attended games make more foul calls than usual, and make the calls when the game is close.
My data will come from a 2005-2006 data set made by www.82games.com, a privately-run NBA statistics website. My data will include fouls by quarter, points by quarter, and officials on a game level. I will use STATA code to transform this dataset to a team level, and analyze it as follows.
I want to run a regression that looks at the foul gap between the home and away teams. Specifically, I will regress a "home/away" variable while controlling for day, opponent fixed effects, and team fixed effects. This will show whether the home team does, in fact, receive more calls.
I also want to regress point differential, both by quarter and game, on this foul gap statistic. I think that the lower the point differential, the higher this foul gap will be--in favor of the home team. Conversely, the higher the point differential, the lower the foul gap.
Finally, I want to regress attendance and referees on this foul gap variable, to see the effect of attendance on foul calls and whether certain officials are especially prone to making calls in big games.
There are two problems I forsee in working with my data set. First, I don't know how to use the officials in the regression. Currently, my data has the names of officials in each game. However, there are rotating groups of 3 officials per NBA game that I don't know how to code for in using in a regression.
Secondly, often there are lots of fouls late in the game by teams trying to catch up by stopping the clock and making the other team shoot foul shots. These fouls could distort my results, and I'm not sure how to control for them.
One thing I just wanted to note was whether or not the official track records differentiated personal fouls and technical fouls. Some instances of unsportsman-like behavior or non-contact fouls may have more to do with the player and coach tempers than with the official (though an easily-annoyed official may call technicals more often). This may exaggerate the number of fouls for close games when one particular team gets more riled up than others.
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