USF St. Petersburg campus Faculty Publications
A data snapshot approach for making real-time predictions in basketball.
Document Type
Article
Publication Date
2018
ISSN
2167-6461
Abstract
This article proposes a novel approach, called data snapshots, to generate real-time probabilities of winning for National Basketball Association (NBA) teams while games are being played. The approach takes a snapshot from a live game, identifies historical games that have the same snapshot, and uses the outcomes of these games to calculate the winning probabilities of the teams in this game as the game is underway. Using data obtained from 20 seasons worth of NBA games, we build three models and compare their accuracies to a baseline accuracy. In Model 1, each snapshot includes the point difference between the home and away teams at a given second of the game. In Model 2, each snapshot includes the net team strength in addition to the point difference at a given second. In Model 3, each snapshot includes the rate of score change in addition to the point difference at a given second. The results show that all models perform better than the baseline accuracy, with Model 1 being the best model.
Publisher
Mary Ann Liebert, Inc. Publishers
Recommended Citation
Kayhan, V.O. & Watkins, A. L. (2018). A data snapshot approach for making real-time predictions in basketball. Big Data, 6(2). doi: 10.1089/big.2017.0054
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
