Spatio-Temporal Analysis of Team Sports
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In this paper, the authors survey recent research efforts that use spatio-temporal data from team sports as input and involve non-trivial computation and identify a number of open research questions.Abstract:
Team-based invasion sports such as football, basketball, and hockey are similar in the sense that the players are able to move freely around the playing area and that player and team performance cannot be fully analysed without considering the movements and interactions of all players as a group. State-of-the-art object tracking systems now produce spatio-temporal traces of player trajectories with high definition and high frequency, and this, in turn, has facilitated a variety of research efforts, across many disciplines, to extract insight from the trajectories. We survey recent research efforts that use spatio-temporal data from team sports as input and involve non-trivial computation. This article categorises the research efforts in a coherent framework and identifies a number of open research questions.read more
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Effective injury forecasting in soccer with GPS training data and machine learning.
Alessio Rossi,Luca Pappalardo,Luca Pappalardo,Paolo Cintia,F. Marcello Iaia,Javier Fernández,Daniel Medina +6 more
TL;DR: This paper proposes a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning, and constructs an injury forecaster that is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners.
Journal ArticleDOI
A public data set of spatio-temporal match events in soccer competitions.
Luca Pappalardo,Paolo Cintia,Alessio Rossi,Emanuele Massucco,Paolo Ferragina,Dino Pedreschi,Fosca Giannotti +6 more
TL;DR: The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide the ideal ground for tackling a wide range of data science problems.
Journal ArticleDOI
State of the Art of Sports Data Visualization
Charles Perin,Charles Perin,Romain Vuillemot,Charles D. Stolper,John Stasko,Jo Wood,Sheelagh Carpendale +6 more
TL;DR: This report analyzes current research contributions through the lens of three categories of sports data: box score data, tracking data, and meta‐data (data about the sport and its participants but not necessarily a given game), identifying critical research gaps and valuable opportunities for the visualization community.
Journal ArticleDOI
PlayeRank: Data-driven Performance Evaluation and Player Ranking in Soccer via a Machine Learning Approach
Luca Pappalardo,Paolo Cintia,Paolo Ferragina,Emanuele Massucco,Dino Pedreschi,Fosca Giannotti +5 more
TL;DR: In this paper, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players is proposed, based on a dataset of players' evaluations made by professional soccer scouts.
Journal ArticleDOI
Influence of Situational Variables, Team Formation, and Playing Position on Match Running Performance and Social Network Analysis in Brazilian Professional Soccer Players
Rodrigo Aquino,Rodrigo Aquino,Christopher Carling,Luiz Henrique Palucci Vieira,Guilherme H. Munhoz Martins,Gustavo Jabor,João Cláudio Machado,Paulo Roberto Pereira Santiago,Júlio Garganta,Enrico Fuini Puggina +9 more
TL;DR: The main results were: no interactive effects between team formation and playing position were observed for running and network variables, and matches played at home or against "weaker" opponents presented greater running demands and individual/global metrics of network analysis.
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