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Predicting Counter-Strike Matches using Machine Learning Models

Broms, Erik LU and Nordansjö, William LU (2024) STAH11 20232
Department of Statistics
Abstract
Sports betting is a widespread industry where predictive modeling play a big role. The goal of this thesis is to explore the possibilities of machine learning within the realm of e-sport prediction. The data used for this thesis is publicly available data was recorded over a three year period. The chosen variables are defined as the difference in player performance between two teams in order to create conditional probabilities. The paper focuses on two machine learning models for evaluating predictability within the data, Logistic regression with parameter regularization and Random Forest. Both models were optimised with cross-validation and their effectiveness is compared to a benchmark which in this case is the betting odds of multiple... (More)
Sports betting is a widespread industry where predictive modeling play a big role. The goal of this thesis is to explore the possibilities of machine learning within the realm of e-sport prediction. The data used for this thesis is publicly available data was recorded over a three year period. The chosen variables are defined as the difference in player performance between two teams in order to create conditional probabilities. The paper focuses on two machine learning models for evaluating predictability within the data, Logistic regression with parameter regularization and Random Forest. Both models were optimised with cross-validation and their effectiveness is compared to a benchmark which in this case is the betting odds of multiple bookmakers. Measures such as accuracy, Log-Loss and the κ-parameter are our main points of comparison. The findings suggest that our models were capable of achieving an accuracy exceeding 50/50 on the test data, implying a certain level of predictability. The models were also applied to a professional tournament, and although a small sample size with large standard errors had an influence, we conclude that the evaluated models did not surpass the performance of the benchmarks. (Less)
Please use this url to cite or link to this publication:
author
Broms, Erik LU and Nordansjö, William LU
supervisor
organization
course
STAH11 20232
year
type
M2 - Bachelor Degree
subject
language
English
id
9145457
date added to LUP
2024-01-24 11:01:20
date last changed
2024-01-24 11:01:20
@misc{9145457,
  abstract     = {{Sports betting is a widespread industry where predictive modeling play a big role. The goal of this thesis is to explore the possibilities of machine learning within the realm of e-sport prediction. The data used for this thesis is publicly available data was recorded over a three year period. The chosen variables are defined as the difference in player performance between two teams in order to create conditional probabilities. The paper focuses on two machine learning models for evaluating predictability within the data, Logistic regression with parameter regularization and Random Forest. Both models were optimised with cross-validation and their effectiveness is compared to a benchmark which in this case is the betting odds of multiple bookmakers. Measures such as accuracy, Log-Loss and the κ-parameter are our main points of comparison. The findings suggest that our models were capable of achieving an accuracy exceeding 50/50 on the test data, implying a certain level of predictability. The models were also applied to a professional tournament, and although a small sample size with large standard errors had an influence, we conclude that the evaluated models did not surpass the performance of the benchmarks.}},
  author       = {{Broms, Erik and Nordansjö, William}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Predicting Counter-Strike Matches using Machine Learning Models}},
  year         = {{2024}},
}