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

TL;DR

PlayeRank is a novel data-driven framework that evaluates soccer players' performance across multiple dimensions and roles using extensive match data, outperforming existing methods and revealing insights about top players.

Contribution

It introduces PlayeRank, a new multi-dimensional, role-aware performance evaluation method for soccer players based on large-scale match data and machine learning techniques.

Findings

PlayeRank outperforms existing performance evaluation algorithms.

It reveals patterns distinguishing top players from others.

The framework is flexible and suitable for scalable soccer analytics applications.

Abstract

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.