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
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.
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