NBA Data Analysis

Awhile ago Mark Cuban posted an interesting break down of some possible inequalities in the NBA scheduling. Basically some teams had more back-to-back games and more set of 4 games in 5 nights. The assumption was that teams would do worse with less rest time.

I was interested to see if that assumption was true. The best data I could get was from Department of Statistics, University of Munich, and the SFB386 which has the entire 95/96 NBA season broken down game by game.

I wrote a simple app to analyse the data (run the test NBATests.DataSetTests.ParseTest to re-run the analysis). The results of the test are in the spread sheet stats.xlsx in the zip file. In that season there didn't appear to be any 4-in-5 sets. That must be a new invention by the NBA.

The numbers are interesting. Overall the win percentage for the league when playing the second game of a back-to-back set is 44%. When taking into account a team's overall win percentage for the season the average difference in win percentage between games that were back-to-back and those where the team had a rest night before was 7%.

8 teams did better in back-to-back games, Miami led the way, their win percentage was 15% higher in their second game.

Atlanta on the other was abysmal, their win percent dropped 39% when they had to play two games in a row.

I had the thought that better teams might be better because of their bench and thus would fair better in back-to-back games because of their depth but this doesn't appear to be the case. The r-squared value for win percent plotted against win percent difference in back-to-back games was only .015.

So overall while some teams appear to get better in back-to-back games overall I think the assumption that it is a disadvantage to have more back-to-back games is a fair one.

There are all kind of other ways to slice up the data that I didn't do but someone should:

• Exclude games where both teams are playing back-to-back.
• Factor in home and away win percentages and not just overall win percentage.
• Doing some kind of qualitative analysis on the depth of the teams that year to see if that plays a factor.

Also if anyone has a data set(s) for more recent years send them over and I'll plug them in and see if these results hold year over year.