# Presenting Multiagent Challenges in Team Sports Analytics

arXiv: https://arxiv.org/abs/2303.13660 · HTML: https://mlsanity.com/paper/2303.13660/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [RUDDER: Return Decomposition for Delayed Rewards](https://mlsanity.com/paper/1806.07857/) (2018)
- [Coordinated Multi-Agent Imitation Learning](https://mlsanity.com/paper/1703.03121/) (2017)
- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [Game Plan: What AI can do for Football, and What Football can do for AI](https://mlsanity.com/paper/2011.09192/) (2020)
- [Reasoning about Hypothetical Agent Behaviours and their Parameters](https://mlsanity.com/paper/1906.11064/) (2019)
- [A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions](https://mlsanity.com/paper/2011.09426/) (2020)
- [Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL](https://mlsanity.com/paper/2207.05683/) (2022)
- [Player Chemistry: Striving for a Perfectly Balanced Soccer Team](https://mlsanity.com/paper/2003.01712/) (2020)
- [The Importance of Credo in Multiagent Learning](https://mlsanity.com/paper/2204.07471/) (2022)
- [Exploring the Benefits of Teams in Multiagent Learning](https://mlsanity.com/paper/2205.02328/) (2022)
- [Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions](https://mlsanity.com/paper/2006.09538/) (2020)
- [Optical tracking in team sports](https://mlsanity.com/paper/2204.04143/) (2022)
- [Evaluating deep tracking models for player tracking in broadcast ice hockey video](https://mlsanity.com/paper/2205.10949/) (2022)

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

- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Simulating Tracking Data to Advance Sports Analytics Research](https://mlsanity.com/paper/2503.19809/) (2025)

## Abstract (arXiv)

This paper draws correlations between several challenges and opportunities within the area of team sports analytics and key research areas within multiagent systems (MAS). We specifically consider invasion games, defined as sports where players invade the opposing team's territory and can interact anywhere on a playing surface such as ice hockey, soccer, and basketball. We argue that MAS is well-equipped to study invasion games and will benefit both MAS and sports analytics fields. Our discussion highlights areas for MAS implementation and further development along two axes: short-term in-game strategy (coaching) and long-term team planning (management).