An Analysis of NBA Spatio-Temporal Data
This project examines the utility of spatio-temporal tracking data from professional basketball games by fitting models predicting whether a player will make a shot. The first part of the project involved the exploration of the data, evaluated its issues, and generated features to use as co-variates in the models. The second part fit various classification models and evaluated their predictive performance. The paper concludes with a discussion of methods to improve the models and future work.
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Rights for Collection: Masters Theses
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info