Statistical Assessment of Soccer Players

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Creator: 

Nabavi, Mohammad-Amin

Date: 

2019

Abstract: 

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.

Subject: 

Statistics

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Science: 
M.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

FLAG

Parent Collection: 

Theses and Dissertations

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