# Player Chemistry: Striving for a Perfectly Balanced Soccer Team

arXiv: https://arxiv.org/abs/2003.01712 · HTML: https://mlsanity.com/paper/2003.01712/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2020.

## Built on by (most cited first, 7 of 7)

- [Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM](https://mlsanity.com/paper/2306.08206/) (2023)
- ["Why Would I Trust Your Numbers?" On the Explainability of Expected Values in Soccer](https://mlsanity.com/paper/2105.13778/) (2021)
- [Learning Football Body-Orientation as a Matter of Classification](https://mlsanity.com/paper/2106.00359/) (2021)
- [Presenting Multiagent Challenges in Team Sports Analytics](https://mlsanity.com/paper/2303.13660/) (2023)
- [Machine Learning for Soccer Match Result Prediction](https://mlsanity.com/paper/2403.07669/) (2024)
- [Better Prevent than Tackle: Valuing Defense in Soccer Based on Graph Neural Networks](https://mlsanity.com/paper/2512.10355/) (2025)
- [Modeling Matches as Language: A Generative Transformer Approach for Counterfactual Player Valuation in Football](https://mlsanity.com/paper/2603.15212/) (2026)

## 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.