# PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach

arXiv: https://arxiv.org/abs/1802.04987 · HTML: https://mlsanity.com/paper/1802.04987/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2018.

## Built on by (most cited first, 10 of 10)

- [ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton](https://mlsanity.com/paper/2112.01044/) (2021)
- [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)
- [Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM](https://mlsanity.com/paper/2306.08206/) (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)
- [Coach2vec: autoencoding the playing style of soccer coaches](https://mlsanity.com/paper/2106.15444/) (2021)
- [6MapNet: Representing soccer players from tracking data by a triplet network](https://mlsanity.com/paper/2109.04720/) (2021)
- [A Multi-stage deep architecture for summary generation of soccer videos](https://mlsanity.com/paper/2205.00694/) (2022)
- [Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super League](https://mlsanity.com/paper/2205.02731/) (2022)
- [A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health](https://mlsanity.com/paper/2411.02594/) (2024)
- [An interactive dashboard for searching and comparing soccer performance scores](https://mlsanity.com/paper/2105.04293/) (2021)

## Abstract (arXiv)

The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this paper, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players' evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by sf PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank -- i.e. searching players and player versatility --- showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.