# Action Valuation in Sports: A Survey

arXiv: https://arxiv.org/abs/2504.06163 · HTML: https://mlsanity.com/paper/2504.06163/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [TacticAI: an AI assistant for football tactics](https://mlsanity.com/paper/2310.10553/) (2023)
- [VARS: Video Assistant Referee System for Automated Soccer Decision Making from Multiple Views](https://mlsanity.com/paper/2304.04617/) (2023)
- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions](https://mlsanity.com/paper/2011.09426/) (2020)
- [Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation](https://mlsanity.com/paper/1805.11088/) (2018)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study](https://mlsanity.com/paper/2103.09627/) (2021)
- [What Happened Next? Using Deep Learning to Value Defensive Actions in Football Event-Data](https://mlsanity.com/paper/2106.01786/) (2021)
- [Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning](https://mlsanity.com/paper/2305.17886/) (2023)
- [Deep learning for action spotting in association football videos](https://mlsanity.com/paper/2410.01304/) (2024)
- [Revisiting Expected Possession Value in Football: Introducing a Benchmark, U-Net Architecture, and Reward and Risk for Passes](https://mlsanity.com/paper/2502.02565/) (2025)

## Built on by (most cited first, 2 of 2)

- [H-VAEP and H-xT: Valuing Offensive On-the-Ball Actions in Handball by Estimating Probabilities](https://mlsanity.com/paper/2608.12926/) (2026)
- [DShuttleNet: A Badminton Doubles Dataset for Evaluating Team Coordination](https://mlsanity.com/paper/2610.14016/) (2026)

## Abstract (arXiv)

Action Valuation (AV) has emerged as a key topic in Sports Analytics, offering valuable insights by assigning scores to individual actions based on their contribution to desired outcomes. Despite a few surveys addressing related concepts such as Player Valuation, there is no comprehensive review dedicated to an in-depth analysis of AV across different sports. In this survey, we introduce a taxonomy with nine dimensions related to the AV task, encompassing data, methodological approaches, evaluation techniques, and practical applications. Through this analysis, we aim to identify the essential characteristics of effective AV methods, highlight existing gaps in research, and propose future directions for advancing the field.