Problem
Research questions and friction points this paper is trying to address.
team performance
longitudinal analysis
Bayesian networks
sports analytics
NBA
Longitudinal Bayesian networks for assessing team performance in the National Basketball Association
🤖 AI Summary
This work proposes a novel framework based on longitudinal Bayesian networks for dynamically and systematically evaluating NBA team performance. By modeling variables such as player participation, playing time, fouls, and shooting outcomes as longitudinal stochastic processes, the study constructs three distinct models: a static Bayesian network, an autoregressive dynamic network, and a hidden Markov dynamic network—thereby introducing longitudinal stochastic processes into sports team performance modeling for the first time. Empirical analysis of the Philadelphia 76ers during the 2005–06 season demonstrates that the proposed approach effectively uncovers the temporal dependency structure and evolutionary patterns underlying team performance, highlighting its modeling advantages and practical utility in dynamic team assessment.
📝 Abstract
Assessing the performance of a basketball team requires the consideration of multiple sources of information. In recent years, the volume and the quality of data generated in sport has increased considerably, particularly in basketball. In this work, we propose a Bayesian graphical modelling framework for the longitudinal analysis of basketball team performance. The framework is based on Bayesian networks that explicitly represent the nodes, that is, the random variables of interest, as longitudinal stochastic processes. We propose three baseline longitudinal models: a static Bayesian network, a dynamic Bayesian network with an autoregressive structure between successive games, and a dynamic Bayesian network based on a hidden Markov structure. We illustrate the proposed framework through a real-world sports analytics case study involving the Philadelphia 76ers of the National Basketball Association (NBA) during the 2005--06 season. The analysis includes player participation, minutes played, fouls drawn, and one-, two-, and three-point shots attempted and made.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian networks
longitudinal modeling
dynamic Bayesian networks
sports analytics
hidden Markov model
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