Statistical Assessment of Soccer Players
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This thesis examines the use of neural network modelling and ordinal logistic regression on a single season of data (2015-16) to score or rank soccer players. These scores and ranks are then compared with ones from FIFA EASports. We also demonstrate the use of association rule mining on one team's data to identify players that are associated with winning (or not winning) a match. Analyses are based on data from the Italian Serie A League 2015-2016 season.
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- Copyright © 2019 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2019
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nabavi-statisticalassessmentofsoccerplayers.pdf | 2023-05-05 | Public | Download |