Optimizing Football Lineup Selection Using Machine Learning

Date

2023-9-5

Author

Göltaş, Yılmaz Taylan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Football has both the biggest economy and the largest audience in the sports world. Billions of dollars change hands every year in line with the decisions made in the sports economy. With the growth of the economic reflections of data decisions, decision systems have become more open to analytical approaches as in other sports. Thanks to increasing data types and developing semi- and fully automated data collection systems, data about both teams and players have become diverse and accessible. Increasing data opportunities have paved the way for on-field and off-field decisions in football to be solved with datacentred approaches. Team selection is one of these decisions. Traditionally, football coaches make this decision by analyzing players' match and training performances and by analyzing the data of the opposing team. In this thesis, a new solution to the team selection problem is proposed with a data-driven approach by using the match data of the players and teams, grouping the players based on their positions and roles, considering the opposing team, tactical formation and environmental factors.

Subject Keywords

Football, Line-up, Decision Support, Optimization, Player Roles

URI

https://hdl.handle.net/11511/105400

Collections

Graduate School of Informatics, Thesis