QPass: a Merit-based Evaluation of Soccer Passes

Autor:	Gyarmati, Laszlo, Stanojevic, Rade
Rok vydání:	2016
Předmět:	Computer Science - Artificial Intelligence Statistics - Applications Statistics - Machine Learning H.4
Druh dokumentu:	Working Paper
Popis:	Quantitative analysis of soccer players' passing ability focuses on descriptive statistics without considering the players' real contribution to the passing and ball possession strategy of their team. Which player is able to help the build-up of an attack, or to maintain the possession of the ball? We introduce a novel methodology called QPass to answer questions like these quantitatively. Based on the analysis of an entire season, we rank the players based on the intrinsic value of their passes using QPass. We derive an album of pass trajectories for different gaming styles. Our methodology reveals a quite counterintuitive paradigm: losing the ball possession could lead to better chances to win a game. Comment: 2016 ACM KDD Workshop on Large-Scale Sports Analytics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1608.03532 Zobrazit plný text záznamu View this record from Arxiv