Concise Analysis of European Club Transfer Market Practices Using Seasonal Performance Analysis

Jaglan, Saurav (2023) Concise Analysis of European Club Transfer Market Practices Using Seasonal Performance Analysis. Masters thesis, Dublin, National College of Ireland.

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Abstract

Football stands as one of the most profitable sports in the world, and recently we have seen football clubs spending generous amounts of money to obtain best talents in the world. This big money spree does not always result in value for money, and clubs might even risk becoming bankrupt. This research involves the implementation of machine learning practices in modern football to strategize financial spendings on players. The research is divided into two parts, in the first, using Random Poisson and KNN for forecasting season end ranking of the leagues, based on the clubs ranking they might identify target player, the model was evaluated using real results by comparing predicted match outcomes vs real match outcomes, and the KNN model also achieved an average accuracy of 78%. Once the 1st stage was complete the 2nd stage aimed in predicting the value of players in different leagues using three different algorithms Gradient Boosting, Random Forest, and Decision Tree. It was found Gradient Boosting was the best suited algorithm which achieved an average accuracy of 91%. By assistance of these two models clubs can simulate their possible season end ranking, and estimate value of target players in different leagues during the transfer window and sign them without overspending.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Staikopoulos, Athanasios UNSPECIFIED
Uncontrolled Keywords:	Football; Transfer market; Value; Player; Machine Learning
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science G Geography. Anthropology. Recreation > GV Recreation Leisure > Sports > Soccer Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning G Geography. Anthropology. Recreation > GV Recreation Leisure > Sports
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	09 May 2025 08:36
Last Modified:	09 May 2025 08:36
URI:	https://norma.ncirl.ie/id/eprint/7531

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