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

arXiv: https://arxiv.org/abs/2011.09192 · HTML: https://mlsanity.com/paper/2011.09192/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 27 of 27)

- [From Motor Control to Team Play in Simulated Humanoid Football](https://mlsanity.com/paper/2105.12196/) (2021)
- [SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries](https://mlsanity.com/paper/2304.04565/) (2023)
- [TacticAI: an AI assistant for football tactics](https://mlsanity.com/paper/2310.10553/) (2023)
- [Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports](https://mlsanity.com/paper/2405.17680/) (2024)
- [Dual networks based 3D Multi-Person Pose Estimation from Monocular Video](https://mlsanity.com/paper/2205.00748/) (2022)
- [Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL](https://mlsanity.com/paper/2207.05683/) (2022)
- [Automatic event detection in football using tracking data](https://mlsanity.com/paper/2202.00804/) (2022)
- [Automated Task-Time Interventions to Improve Teamwork using Imitation Learning](https://mlsanity.com/paper/2303.00413/) (2023)
- [A Strategic Framework for Optimal Decisions in Football 1-vs-1 Shot-Taking Situations: An Integrated Approach of Machine Learning, Theory-Based Modeling, and Game Theory](https://mlsanity.com/paper/2307.14732/) (2023)
- [Data-driven Analysis for Understanding Team Sports Behaviors](https://mlsanity.com/paper/2102.07545/) (2021)
- [Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis](https://mlsanity.com/paper/2111.15186/) (2021)
- [Multi-Agent Diagnostics for Robustness via Illuminated Diversity](https://mlsanity.com/paper/2401.13460/) (2024)
- [TacEleven: generative tactic discovery for football open play](https://mlsanity.com/paper/2511.13326/) (2025)
- [PlayBest: Professional Basketball Player Behavior Synthesis via Planning with Diffusion](https://mlsanity.com/paper/2306.04090/) (2023)
- [Offline Equilibrium Finding](https://mlsanity.com/paper/2207.05285/) (2022)
- [Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments](https://mlsanity.com/paper/2209.10958/) (2022)
- [A Survey of Advanced Computer Vision Techniques for Sports](https://mlsanity.com/paper/2301.07583/) (2023)
- [Presenting Multiagent Challenges in Team Sports Analytics](https://mlsanity.com/paper/2303.13660/) (2023)
- [pix2pockets: Shot Suggestions in 8-Ball Pool from a Single Image in the Wild](https://mlsanity.com/paper/2504.12045/) (2025)
- [Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage](https://mlsanity.com/paper/2202.12259/) (2022)
- [Building Scalable Video Understanding Benchmarks through Sports](https://mlsanity.com/paper/2301.06866/) (2023)
- [A Data-Driven Discretized CS:GO Simulation Environment to Facilitate Strategic Multi-Agent Planning Research](https://mlsanity.com/paper/2509.06355/) (2025)
- [A Machine Learning Framework for Off Ball Defensive Role and Performance Evaluation in Football](https://mlsanity.com/paper/2601.00748/) (2026)
- [What AI can do for horse-racing ?](https://mlsanity.com/paper/2207.04981/) (2022)
- [Stop Guessing: Optimizing Goalkeeper Policies for Soccer Penalty Kicks](https://mlsanity.com/paper/2505.24629/) (2025)
- [Coherent Multi-Agent Trajectory Forecasting in Team Sports with CausalTraj](https://mlsanity.com/paper/2511.18248/) (2025)
- [A Universal Dense Football Event Representation Based on TabTransformer](https://mlsanity.com/paper/2606.09327/) (2026)

## Abstract (arXiv)

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).