Friday, May 13, 2011

Initial Analysis

My efforts to define chemistry in basketball began with research for my Masters in Sport Management at the University of San Francisco, which I presented via poster at the 2010 Northern California Symposium on Statistics and Operations Research in Sports (NCSSORS), held by Dr. Ben Alamar, the Director of Basketball Analytics and Research for the Oklahoma City Thunder, and Peter Keating later wrote about in his blog for ESPN 

With wins being a function of offensive and defensive efficiency, this study sought to predict offensive efficiency in terms of how various statistics or roles are distributed among the players on the floor, such as shooting from the three point line, field generally and free throw line, passing, and offensive rebounding using a multiple linear regression model.  This was accomplished by using the standard deviations of the pace- and minute-adjusted field goals attempted, free throws attempted, three-point field goals attempted, offensive rebounds, assists and turnovers among the top five or top eight players in minutes played to represent how those roles are distributed and to predict field goals made, free throws made, three-point field goals made and turnovers, the significant variables in predicting offensive efficiency.

NBA regular season team and player data since 1997 was gathered from and used to create the data sets for this study.  370 teams were observed.  The 1997-1998 season was chosen as the initial season in the data set as it was when the three-point line was moved back to 23’9”, where it has since remained.  Team and player stats were adjusted for team pace and minutes played.  

The charts on the right side of the poster display the following results:  A wider distribution of three-point attempts predicts an increase of as much as 4 points per 100 possessions.   A wider distribution of free throw attempted predicts an increase of as much as 3 points per 100 possessions.  A wider distribution of assists predicts an increase of as much as 2 points per 100 possessions.  A more even distribution of turnovers predicts an increase of as much as 2 points per 100 possessions.  A more even distribution of offensive rebounds predicts an increase of as much as 4 points per 100 possessions.  The distribution of field goal attempts was not significant in this analysis.  

There are some significant differences in points scored between the widest and most even distribution of various statistical categories.  With the exception of offensive rebounds and turnovers, a wider distribution proved more beneficial, indicating that teams built with more defined roles in shooting and distributing are more efficient.  This is just the beginning of this analysis and the implication of the results of analyzing how roles are distributed deserves more thought, as Kevin Pelton suggested in his recap of the NCSSORS conference.  

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