Timezone: »
A major task of sports analytics is player evaluation. Previous methods commonly measured the impact of players' actions on desirable outcomes (e.g., goals or winning) without considering the risk induced by stochastic game dynamics. In this paper, we design an uncertainty-aware Reinforcement Learning (RL) framework to learn a risk-sensitive player evaluation metric from stochastic game dynamics. To embed the risk of a player’s movements into the distribution of action-values, we model their 1) aleatoric uncertainty, which represents the intrinsic stochasticity in a sports game, and 2) epistemic uncertainty, which is due to a model's insufficient knowledge regarding Out-of-Distribution (OoD) samples. We demonstrate how a distributional Bellman operator and a feature-space density model can capture these uncertainties. Based on such uncertainty estimation, we propose a Risk-sensitive Game Impact Metric (RiGIM) that measures players' performance over a season by conditioning on a specific confidence level. Empirical evaluation, based on over 9M play-by-play ice hockey and soccer events, shows that RiGIM correlates highly with standard success measures and has a consistent risk sensitivity.
Author Information
Guiliang Liu (The Chinese University of Hong Kong, Shenzhen)
Yudong Luo (University of Waterloo)
Oliver Schulte (Simon Fraser University)

Bio: Oliver Schulte is a Professor in the School of Computing Science at Simon Fraser University, Vancouver, Canada. He received his Ph.D. from Carnegie Mellon University in 1997. His current research focuses on machine learning for structured, relational, and event data. He has published sports analytics papers in leading AI and machine learning venues, and co-organized two hockey analytics conferences. The last two years he has worked with Sportlogiq, a leading hockey data provider. While he has won some nice awards, his biggest claim to fame may be a draw against chess world champion Gary Kasparov.
Pascal Poupart (University of Waterloo & Vector Institute)
More from the Same Authors
-
2022 Poster: Optimality and Stability in Non-Convex Smooth Games »
Guojun Zhang · Pascal Poupart · Yaoliang Yu -
2022 : Attribute Controlled Dialogue Prompting »
Runcheng Liu · Ahmad Rashid · Ivan Kobyzev · Mehdi Rezaghoizadeh · Pascal Poupart -
2022 : Geometric attacks on batch normalization »
Amur Ghose · Apurv Gupta · Yaoliang Yu · Pascal Poupart -
2023 Poster: Batchnorm Allows Unsupervised Radial Attacks »
Amur Ghose · Apurv Gupta · Yaoliang Yu · Pascal Poupart -
2023 Poster: Neural Graph Generation from Graph Statistics »
Kiarash Zahirnia · Yaochen Hu · Mark Coates · Oliver Schulte -
2023 Poster: An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient »
Yudong Luo · Guiliang Liu · Pascal Poupart · Yangchen Pan -
2023 Poster: Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations »
Guanren Qiao · Guiliang Liu · Pascal Poupart · Zhiqiang Xu -
2023 Poster: Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing »
Xiangyu Sun · Oliver Schulte -
2022 Spotlight: Optimality and Stability in Non-Convex Smooth Games »
Guojun Zhang · Pascal Poupart · Yaoliang Yu -
2022 Spotlight: Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders »
Kiarash Zahirnia · Oliver Schulte · Parmis Naddaf · Ke Li -
2022 : Attribute Controlled Dialogue Prompting »
Runcheng Liu · Ahmad Rashid · Ivan Kobyzev · Mehdi Rezaghoizadeh · Pascal Poupart -
2022 Workshop: Second Workshop on Efficient Natural Language and Speech Processing (ENLSP-II) »
Mehdi Rezagholizadeh · Peyman Passban · Yue Dong · Lili Mou · Pascal Poupart · Ali Ghodsi · Qun Liu -
2022 Poster: Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders »
Kiarash Zahirnia · Oliver Schulte · Parmis Naddaf · Ke Li -
2021 : Best Papers and Closing Remarks »
Ali Ghodsi · Pascal Poupart -
2021 : Panel Discussion »
Pascal Poupart · Ali Ghodsi · Luke Zettlemoyer · Sameer Singh · Kevin Duh · Yejin Choi · Lu Hou -
2021 Workshop: Efficient Natural Language and Speech Processing (Models, Training, and Inference) »
Mehdi Rezaghoizadeh · Lili Mou · Yue Dong · Pascal Poupart · Ali Ghodsi · Qun Liu -
2021 : Opening Speech »
Pascal Poupart -
2021 Poster: Quantifying and Improving Transferability in Domain Generalization »
Guojun Zhang · Han Zhao · Yaoliang Yu · Pascal Poupart -
2021 Poster: Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning »
Guiliang Liu · Xiangyu Sun · Oliver Schulte · Pascal Poupart -
2020 Poster: Learning Agent Representations for Ice Hockey »
Guiliang Liu · Oliver Schulte · Pascal Poupart · Mike Rudd · Mehrsan Javan -
2020 Poster: Learning Dynamic Belief Graphs to Generalize on Text-Based Games »
Ashutosh Adhikari · Xingdi Yuan · Marc-Alexandre Côté · Mikuláš Zelinka · Marc-Antoine Rondeau · Romain Laroche · Pascal Poupart · Jian Tang · Adam Trischler · Will Hamilton -
2018 Workshop: Reinforcement Learning under Partial Observability »
Joni Pajarinen · Chris Amato · Pascal Poupart · David Hsu -
2018 Poster: Deep Homogeneous Mixture Models: Representation, Separation, and Approximation »
Priyank Jaini · Pascal Poupart · Yaoliang Yu -
2018 Poster: Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks »
Agastya Kalra · Abdullah Rashwan · Wei-Shou Hsu · Pascal Poupart · Prashant Doshi · George Trimponias -
2018 Poster: Unsupervised Video Object Segmentation for Deep Reinforcement Learning »
Vikash Goel · Jameson Weng · Pascal Poupart -
2018 Poster: Monte-Carlo Tree Search for Constrained POMDPs »
Jongmin Lee · Geon-Hyeong Kim · Pascal Poupart · Kee-Eung Kim -
2016 Poster: Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics »
Wei-Shou Hsu · Pascal Poupart -
2016 Poster: A Unified Approach for Learning the Parameters of Sum-Product Networks »
Han Zhao · Pascal Poupart · Geoffrey Gordon