Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation

Liu, Guiliang; Schulte, Oliver

doi:10.24963/ijcai.2018/478

Cited by 60 publications

(59 citation statements)

References 12 publications

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“…4 We do not restrict the warping paths in the distance computations. Inspired by the work from Liu and Schulte on evaluating player performances in ice hockey, we evaluate our approach by predicting the outcomes of future games as we expect our pass values to be predictors of future performances [10]. We predict the outcomes for 1,172 games in the English Premier League, Spanish LaLiga, German 1.…”

Section: Methodsmentioning

confidence: 99%

Measuring Football Players’ On-the-Ball Contributions from Passes During Games

Bransen¹,

Haaren²

2019

Machine Learning and Data Mining for Sports Analytics

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Several performance metrics for quantifying the in-game performances of individual football players have been proposed in recent years. Although the majority of the on-the-ball actions during games constitutes of passes, many of the currently available metrics focus on measuring the quality of shots only. To help bridge this gap, we propose a novel approach to measure players' on-the-ball contributions from passes during games. Our proposed approach measures the expected impact of each pass on the scoreline.

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Section: Methodsmentioning

confidence: 99%

Measuring Football Players’ On-the-Ball Contributions from Passes During Games

Bransen¹,

Haaren²

2019

Machine Learning and Data Mining for Sports Analytics

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“…The combination of RL techniques with deep learning has great potential to tackle the idiosyncrasies of football and to close the gap between statistical and human analysis. It is exciting to see recent progress in this direction in sports analytics, including ice hockey (Liu & Schulte, 2018) and football (Liu, Luo, Schulte, & Kharrat, 2020;X. Sun, Davis, Schulte, & Liu, 2020) (with additional related works detailed in Appendix A).…”

Section: Statistical Learningmentioning

confidence: 99%

Game Plan: What AI can do for Football, and What Football can do for AI

Tuyls

Omidshafiei²,

Müller³

et al. 2021

jair

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The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

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“…The mainstream approaches for performance evaluation are to assess player's performance by estimating the probability of the latest several goal attempts resulting in a goal, called expected-goals models [2,3,6,9,14,18]. In this case, the whole match session is divided into several fragments base on the 'goal' event, and only the latest several on-ball actions of a goal are considered.…”

Section: Performance Evaluationmentioning

confidence: 99%

Match Tracing

Wang

Gong

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Win prediction and performance evaluation are two core subjects in the sport analytics. Traditionally, they are treated separately and studied by two independent communities. However, this is not the intuitive way how humans interpret the matches: we predict the match results with the competition carrying on, and simultaneously evaluate each action based on the game context and its downstream impact. Predicting the match outcomes and evaluating the actions are coupled tasks, and the more accurately we predict, the better the evaluation is. To this end, we develop a unified Match Tracing framework (namely, MT), for tackling the win prediction and performance evaluation jointly. The main idea of MT is to learn a real-time lookahead win rate curve rather than a single scalar (win or lose). And the value of an action can be objectively measured with respect to the increase or decrease of the curve. To meet the low-latency restrictions of the online deployments, an efficient model equipped with recurrent attention mechanism and matrix perturbation (i.e., MT-Net) is built for learning and yielding the win rate curve. MT-Net encodes the players' behavior sequence through an attention mechanism and captures the player-interaction effects through a graph embedding method. With the action values derived from the win rate curve, performance can be quantified at different granularities (action/player/match level) by integrated analysis. Experiments on an e-sport game demonstrate the prediction effectiveness and the feasibility of the MT framework. Furthermore, we present the detailed application cases of MT, including key actions recognition and close match detection.

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Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation

Cited by 60 publications

References 12 publications

Measuring Football Players’ On-the-Ball Contributions from Passes During Games

Measuring Football Players’ On-the-Ball Contributions from Passes During Games

Game Plan: What AI can do for Football, and What Football can do for AI

Match Tracing

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