Abstract
This paper represents a final group assignment report, submitted by four strangers (before the assignment was started) to the Mathematical Modelling of Football course run out of Uppsala University in 2020. This accredited course is a comprehensive educational course on how to work with football data and understanding the game using maths and statistics. The challenge of the final group assignment was to take on the persona of a data scientists working for a leading football club, and to prepare an end of season report on player fitness. The assignment also required that a software model of acceleration/deceleration profiles of club players be created and to cluster the players in the assigned football team (Inter Milan season 2019-2020) on the basis of these profiles. The assignment was to consider combining player fitness performance data over time during the match in the analysis and to discuss the different types of players in the football squad in relation to players in squads of other leading European teams. The source tracking data used is extracted by AI systems from broadcast video footage of the match.
Presenters
Miguel Ponce De LeonTechnology Gateway Manager, Walton Institute, Waterford Institute of Technolgy, Waterford, Ireland Karim Elgammal
KTH Royal Institute of Technology and Swedish e-Science Research Centre Dimitrios Bampalikis
Systems Developer, NBIS, Sweden Staffan Nöteberg
Consultant, Rekursiv, Sweden
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Football, Soccer, Data. Maths, Statistics, Fitness, Performance, Software, Model
