Bancheri, Andreas (2022) Exploration of Throw-Ins in Soccer Using Machine Learning Algorithms. Masters thesis, Concordia University.
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Abstract
The evolving field of sports analytics is still in the early stages of its adoption. Moreover, soccer analytics utilizing tracking data is even further limited. This research is motivated by Liverpool's integration of a department for throw-in research, assisting them in winning a league title. This research project makes use of the (generously given) German national soccer team (DFB) tracking and event data which includes all player movement during a game, and more specifically, movement before and after a throw-in.
The probability of a throw-in being completed (according to two mutually exclusive definitions) is estimated using various metrics developed using the aforementioned tracking and event data. Binary classification models are used to estimate the completion probability of a given throw-in. The results show that the model provides an encouraging framework of achieving the goal of a universal throw-in metric. Therefore, any given throw-in may be evaluated, providing a meaningful tool to soccer teams, in the footsteps of xG (expected goal) or xPass (expected pass) models.
Divisions: | Concordia University > Faculty of Arts and Science > Mathematics and Statistics |
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Item Type: | Thesis (Masters) |
Authors: | Bancheri, Andreas |
Institution: | Concordia University |
Degree Name: | M. Sc. |
Program: | Mathematics |
Date: | 13 December 2022 |
Thesis Supervisor(s): | Godin, Frédéric and Smith, Joshua |
ID Code: | 991429 |
Deposited By: | Andreas Bancheri |
Deposited On: | 21 Jun 2023 14:47 |
Last Modified: | 21 Jun 2023 14:47 |
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