PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach Luca Pappalardo∗ Paolo Cintia† Paolo Ferragina Institute of Information Science and Department of Computer Science, Department of Computer Science, Techologies (ISTI), CNR University of Pisa University of Pisa Pisa, Italy Pisa, Italy Pisa, Italy
[email protected] [email protected] [email protected] Emanuele Massucco Dino Pedreschi Fosca Giannotti Wyscout Department of Computer Science, Institute of Information Science and Chiavari, Italy University of Pisa Techologies (ISTI), CNR
[email protected] Pisa, Italy Pisa, Italy
[email protected] [email protected] ABSTRACT 1 INTRODUCTION The problem of evaluating the performance of soccer players is Rankings of soccer players and data-driven evaluations of their attracting the interest of many companies and the scientific com- performance are becoming more and more central in the soccer munity, thanks to the availability of massive data capturing all the industry [5, 12, 28, 33]. On the one hand, many sports companies, events generated during a match (e.g., tackles, passes, shots, etc.). websites and television broadcasters, such as Opta, WhoScored.com Unfortunately, there is no consolidated and widely accepted metric and Sky, as well as the plethora of online platforms for fantasy for measuring performance quality in all of its facets. In this paper, football and e-sports, widely use soccer statistics to compare the we design and implement PlayeRank, a data-driven framework that performance of professional players, with the purpose of increasing offers a principled multi-dimensional and role-aware evaluation of fan engagement via critical analyses, insights and scoring patterns.