[2E1-GS-10] AI application:

[2E1-GS-10-03] Estimation of temporal changes in basketball players' contribution and compatibility based on a state-space model

〇Kazuhiro Yamada¹, Keisuke Fujii^1,2 (1. Nagoya University, 2. RIKEN)

Keywords:State-Space Model, Sports, Bayesian modeling

In professional sports, the correct evaluation of player contributions and compatibility is an important issue. Metrics that quantitatively evaluate them can be useful information for team decision-makers. Existing metrics often overlook temporal changes in player contributions and compatibility, which may arise from player growth and other dynamic factors. Therefore, the purpose of this study is to propose quantitative evaluation metrics that take into account temporal changes in player contribution and compatibility. First, we created time-by-time design matrices using play-by-play data from the 2022-23 and 2023-24 seasons of the B.LEAGUE. Next, the parameters of the state-space model were Bayesian estimated from the design matrices, and the evaluation values of the proposed metrics Gaussian Random Walk Plus-Minus (GRWPM) for players, duos, lineups, and teams were calculated based on the estimated parameters. As a result, the proposed metric showed a strong positive correlation with the existing metric, suggesting its validity to a certain degree. We also discussed its properties and confirmed that it provides information that cannot be captured by the existing metrics alone. The proposed metrics are expected to be useful for scouting players, determining annual salaries and contract years, and considering roster and lineup composition.