# Location analysis of players in UEFA EURO 2020 and 2022 using generalized valuation of defense by estimating probabilities

arXiv: https://arxiv.org/abs/2212.00021 · HTML: https://mlsanity.com/paper/2212.00021/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos](https://mlsanity.com/paper/2204.06918/) (2022)
- [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)
- [PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach](https://mlsanity.com/paper/1802.04987/) (2018)
- [Data-driven Analysis for Understanding Team Sports Behaviors](https://mlsanity.com/paper/2102.07545/) (2021)

## Built on by (most cited first, 8 of 8)

- [Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis](https://mlsanity.com/paper/2302.09276/) (2023)
- [Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning](https://mlsanity.com/paper/2305.17886/) (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)
- [Foul prediction with estimated poses from soccer broadcast video](https://mlsanity.com/paper/2402.09650/) (2024)
- [OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in Soccer](https://mlsanity.com/paper/2502.02785/) (2025)
- [Space evaluation at the starting point of soccer transitions](https://mlsanity.com/paper/2505.14711/) (2025)
- [Expandable Decision-Making States for Multi-Agent Deep Reinforcement Learning in Soccer Tactical Analysis](https://mlsanity.com/paper/2510.00480/) (2025)
- [Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football](https://mlsanity.com/paper/2610.05961/) (2026)

## Abstract (arXiv)

Analyzing defenses in team sports is generally challenging because of the limited event data. Researchers have previously proposed methods to evaluate football team defense by predicting the events of ball gain and being attacked using locations of all players and the ball. However, they did not consider the importance of the events, assumed the perfect observation of all 22 players, and did not fully investigated the influence of the diversity (e.g., nationality and sex). Here, we propose a generalized valuation method of defensive teams by score-scaling the predicted probabilities of the events. Using the open-source location data of all players in broadcast video frames in football games of men's Euro 2020 and women's Euro 2022, we investigated the effect of the number of players on the prediction and validated our approach by analyzing the games. Results show that for the predictions of being attacked, scoring, and conceding, all players' information was not necessary, while that of ball gain required information on three to four offensive and defensive players. With game analyses we explained the excellence in defense of finalist teams in Euro 2020. Our approach might be applicable to location data from broadcast video frames in football games.