# Data-driven Analysis for Understanding Team Sports Behaviors

arXiv: https://arxiv.org/abs/2102.07545 · HTML: https://mlsanity.com/paper/2102.07545/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 132 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 27 of 27)

- [Neural Relational Inference for Interacting Systems](https://mlsanity.com/paper/1802.04687/) (2018)
- [Google Research Football: A Novel Reinforcement Learning Environment](https://mlsanity.com/paper/1907.11180/) (2019)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition](https://mlsanity.com/paper/1710.04340/) (2017)
- [A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning](https://mlsanity.com/paper/1710.05741/) (2017)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Coordinated Multi-Agent Imitation Learning](https://mlsanity.com/paper/1703.03121/) (2017)
- [NAOMI: Non-Autoregressive Multiresolution Sequence Imputation](https://mlsanity.com/paper/1901.10946/) (2019)
- [VAIN: Attentional Multi-agent Predictive Modeling](https://mlsanity.com/paper/1706.06122/) (2017)
- [Generating Multi-Agent Trajectories using Programmatic Weak Supervision](https://mlsanity.com/paper/1803.07612/) (2018)
- [Game Plan: What AI can do for Football, and What Football can do for AI](https://mlsanity.com/paper/2011.09192/) (2020)
- [Generative Modeling of Multimodal Multi-Human Behavior](https://mlsanity.com/paper/1803.02015/) (2018)
- [Generating Long-term Trajectories Using Deep Hierarchical Networks](https://mlsanity.com/paper/1706.07138/) (2017)
- [Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation](https://mlsanity.com/paper/1805.11088/) (2018)
- [Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables](https://mlsanity.com/paper/1808.10551/) (2018)
- [Cracking the Black Box: Distilling Deep Sports Analytics](https://mlsanity.com/paper/2006.04551/) (2020)
- [Optimising Game Tactics for Football](https://mlsanity.com/paper/2003.10294/) (2020)
- [Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball](https://mlsanity.com/paper/1401.0942/) (2014)
- [Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories](https://mlsanity.com/paper/1710.02255/) (2017)
- [The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA](https://mlsanity.com/paper/1803.02940/) (2018)
- [tHoops: A Multi-Aspect Analytical Framework Spatio-Temporal Basketball Data](https://mlsanity.com/paper/1712.01199/) (2017)
- [Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis](https://mlsanity.com/paper/2002.05578/) (2020)
- [BasketballGAN: Generating Basketball Play Simulation Through Sketching](https://mlsanity.com/paper/1909.07088/) (2019)
- [Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union](https://mlsanity.com/paper/2010.15377/) (2020)
- [Succinct Trit-array Trie for Scalable Trajectory Similarity Search](https://mlsanity.com/paper/2005.10917/) (2020)
- [Multi-resolution Tensor Learning for Large-Scale Spatial Data](https://mlsanity.com/paper/1802.06825/) (2018)
- [Improved Structural Discovery and Representation Learning of Multi-Agent Data](https://mlsanity.com/paper/1912.13107/) (2019)

## Built on by (most cited first, 6 of 6)

- [Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction](https://mlsanity.com/paper/2206.01899/) (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)
- [Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning](https://mlsanity.com/paper/2305.17886/) (2023)
- [Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations](https://mlsanity.com/paper/2305.13030/) (2023)
- [Location analysis of players in UEFA EURO 2020 and 2022 using generalized valuation of defense by estimating probabilities](https://mlsanity.com/paper/2212.00021/) (2022)
- [OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in Soccer](https://mlsanity.com/paper/2502.02785/) (2025)

## Abstract (arXiv)

Understanding the principles of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields. The rules regarding the real-world biological multi-agent behaviors such as team sports are often largely unknown due to their inherently higher-order interactions, cognition, and body dynamics. Estimation of the rules from data, i.e., data-driven approaches such as machine learning, provides an effective way for the analysis of such behaviors. Although most data-driven models have non-linear structures and high prediction performances, it is sometimes hard to interpret them. This survey focuses on data-driven analysis for quantitative understanding of invasion team sports behaviors such as basketball and football, and introduces two main approaches for understanding such multi-agent behaviors: (1) extracting easily interpretable features or rules from data and (2) generating and controlling behaviors in visually-understandable ways. The first approach involves the visualization of learned representations and the extraction of mathematical structures behind the behaviors. The second approach can be used to test hypotheses by simulating and controlling future and counterfactual behaviors. Lastly, the potential practical applications of extracted rules, features, and generated behaviors are discussed. These approaches can contribute to a better understanding of multi-agent behaviors in the real world.