My senior research and writing project at the College of Charleston is complete, and the result of it is my bachelor’s essay. I’ve titled it “Modeling Basketball’s Points per Possession With Application to Predicting the Outcome of College Basketball Games”, and the associated abstract for the paper is below:

In this paper we consider how to model basketball’s points per possession data, and we show that the flexibility provided by a multinomial logistic regression is required for modeling this type of data. We show how to apply this model to ranking college basketball teams, and a method for estimating team win probabilities with this model is provided. We show how to use these win probabilities to fill out an NCAA tournament bracket, and we compare the results of filling out tournament brackets with the multinomial model to the results of a simpler model. We find neither model to be better than the other at predicting NCAA tournament games (in terms of statistical significance).

The paper can be downloaded at

Also, some data associated with the analyses in the paper can be downloaded at

Going into this project I was unsure how to best model points per possession data, and looking back now it probably seems rather obvious. That said, I certainly learned a lot, and I hope this will be useful to the community. The section on ranking college basketball teams can also be applied to the NBA, and it is what I use to generate my NBA power rankings.

I would like to thank my advisors Dr. Amy Langville and Dr. Martin Jones for their help and guidance throughout this project. It certainly wouldn’t have been possible without their thoughtful insight and support.

I also want to thank @EdKupfer for reading through and providing feedback at the last moment to help me make sure I didn’t make any obvious mistakes. I know others of you read the paper as well, and I thank you too, for surely you would have pointed out any obvious issues, no? 🙂

I plan to continue developing this paper for hopeful publication, so I am certainly interested in hearing any suggestions for improvement.


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