PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach release_zzqhl74ib5brfpq2mrymyz42xe

by Luca Pappalardo and Paolo Cintia and Paolo Ferragina and Emanuele Massucco and Dino Pedreschi and Fosca Giannotti

Released as a article .

2018  

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 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.
In text/plain format

Archived Files and Locations

application/pdf  2.9 MB
file_24rtucu5pzfxnbjuvpalzkrsw4
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2018-02-14
Version   v1
Language   en ?
arXiv  1802.04987v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 2b6ab13b-77ab-4a47-8172-1453d007b6e5
API URL: JSON