Player Chemistry: Striving for a Perfectly Balanced Soccer Team
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Abstract (arXiv)
Soccer scouts typically ignore the team balance and team chemistry when evaluating potential signings for their teams. Instead, they focus on the individual qualities of the players in isolation. To overcome this limitation of their recruitment process, this paper takes a first step towards objectively providing insight into the question: How well does a team of soccer players gel? We address that question in both an observational and a predictive setting. In the former setting, we observe the chemistry between players who have actually played together, which is relevant when selecting the best possible line-up for a match. In the latter setting, we predict the chemistry between players who have never played together before, which is particularly relevant to assess the fit of a potential signing with the players who are already on the team. We introduce two chemistry metrics that measure the offensive and defensive chemistry for a pair of players, respectively. The offensive chemistry metric measures the pair's joint performance in terms of scoring goals, whereas the defensive chemistry metric measures their joint performance in preventing their opponents from scoring goals. We compute our metrics for 361 seasons in 106 different competitions and present a number of concrete use cases. For instance, we show that the partnership between Mohamed Salah and Roberto Firmino in Liverpool's 2017/2018 Champions League campaign exhibited the highest mutual chemistry between two players. Furthermore, we show that Mesut "Ozil's chemistry has rapidly started declining following Alexis S'anchez' departure to Manchester United in 2018.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 1; 2025: 1; 2026: 1.
7 papers.
- 2023Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTMcited by 12
- 2021"Why Would I Trust Your Numbers?" On the Explainability of Expected Values in Soccercited by 5
- 2021Learning Football Body-Orientation as a Matter of Classificationcited by 2
- 2023Presenting Multiagent Challenges in Team Sports Analyticscited by 2
- 2024Machine Learning for Soccer Match Result Predictioncited by 2
- 2025Better Prevent than Tackle: Valuing Defense in Soccer Based on Graph Neural Networkscited by 2
- 2026Modeling Matches as Language: A Generative Transformer Approach for Counterfactual Player Valuation in Footballcited by 0
- 2026Modeling Matches as Language: A Generative Transformer Approach for Counterfactual Player Valuation in Football16 Mar 2026
- 2025Better Prevent than Tackle: Valuing Defense in Soccer Based on Graph Neural Networks11 Dec 2025
- 2024Machine Learning for Soccer Match Result Prediction12 Mar 2024
- 2023Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM14 Jun 2023
- 2023Presenting Multiagent Challenges in Team Sports Analytics23 Mar 2023
- 2021Learning Football Body-Orientation as a Matter of Classification1 Jun 2021
- 2021"Why Would I Trust Your Numbers?" On the Explainability of Expected Values in Soccer27 May 2021