Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
The Elo rating system has been used in various sports / games, such as chess, soccer, tennis and even video games, to calculate the relative playing strengths of players / teams. Originally, the...Show moreThe Elo rating system has been used in various sports / games, such as chess, soccer, tennis and even video games, to calculate the relative playing strengths of players / teams. Originally, the Elo system was invented by a Hungarian physics professor, Arpad Elo, to improve chess rating system. Now many rating systems used in sports are based on the Elo rating system with modifications. The objective of this thesis project is to examine the Elo rating system for soccer tournaments and how it can be applied to the 2017 UEFA Women’s Championship (short for UEFA Women’s Euro 2017). More specifically, two primary interests lie in this project. The first interest lies in determining the strength of each team by assigning an Elo rating to the each competing team after tournament. In addition, it is interesting to see how home-field advantage helped the Netherlands (the host country) win the championship of UEFA Women’s Euro 2017 by incorporating the home-field advantage in the Elo formula. Secondly, strengths of the players of all teams are also of interest. In order to estimate the strengths of the players, each player is assigned a rating (Not an Elo rating) to represent how strong every player is. We can then compare the players among all teams. In order to access the reliability of our ideas and methodology, a simulation study will follow after the theoretical part of our research. In Chapter 1 I will first describe the basic concepts of the Elo rating system. Then a short summary of the relevant literature papers will be presented. Finally I will discuss the source of the data, the arrangement of the tournament, and the process that will take to go through the algorithm / methodology. In Chapter 2 the basic Elo formula and some modified Elo models are proposed, which allows us later on to determine the most appropriate model for estimating the strengths of every single competing country and the players of all teams. In the end of this chapter, I develop an ordered probit regression model for forecasting match results in UEFA Women’s Euro 2017. Chapter 3 suggests a simulation study for estimating the strengths of all the participant countries of the tournament and the strengths of football players of all teams. Chapter 4 presents the main conclusions drawn from the model computations and suggests some further research of this thesis project.Show less