Basketball is one of the most popular types of sports in the world. Recent technological developments have made it possible to collect large amounts of data on the game, analyze it, and discover new insights. We propose a novel approach for modeling basketball games using deep reinforcement learning. By analyzing multiple aspects of both the players and the game, we are able to model the latent connections among players’ movements, actions, and performance, into a single measure – the Q-Ball. Using Q-Ball, we are able to assign scores to the performance of both players and whole teams. Our approach has multiple practical applications, including evaluating and improving players’ game decisions and producing tactical recommendations. We train and evaluate our approach on a large dataset of National Basketball Association games, and show that the Q-Ball is capable of accurately assessing the performance of players and teams. Furthermore, we show that Q-Ball is highly effective in recommending alternatives to players’ actions.
|Title of host publication||AAAI-22 Technical Tracks 8|
|Publisher||Association for the Advancement of Artificial Intelligence|
|Number of pages||8|
|ISBN (Electronic)||1577358767, 9781577358763|
|State||Published - 30 Jun 2022|
|Event||36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online|
Duration: 22 Feb 2022 → 1 Mar 2022
|Name||Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022|
|Conference||36th AAAI Conference on Artificial Intelligence, AAAI 2022|
|Period||22/02/22 → 1/03/22|
Bibliographical notePublisher Copyright:
Copyright © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence