Win probability estimation for strategic decision-making in esports
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School of Science |
Master's thesis
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Date
2024-08-26
Department
Major/Subject
Systems and Operations Research
Mcode
Degree programme
Master's Programme in Mathematics and Operations Research
Language
en
Pages
38
Series
Abstract
Esports, i.e., the competitive practice of video games, has grown significantly during the past decade, giving rise to esports analytics, a subfield of sports analytics. Due to the digital nature of esports, esports analytics benefits from easier data collection compared to its physical predecessor. However, strategy optimization, one of the focal points of sports analytics, remains relatively unexplored in esports. In traditional sports analytics, win probability estimation has been used for decades to evaluate players and support strategic decision-making. This thesis explores the use of win probability estimation in esports, focusing specifically on League of Legends (LoL), one of the most popular esports games in the world. The objective of this thesis is to formalize win probability added, i.e., the change in win probability associated with a certain action, as a contextualized measure of value for strategic decision-making, using mathematical notation appropriate for contemporary esports. The proposed method is elaborated by applying it to the evaluation of items, a strategic problem in LoL. To this end, we train a deep neural network to estimate the win probability at any given LoL game state. This in-game win probability model is then benchmarked against similar models.Description
Supervisor
Salo, AhtiThesis advisor
Struckmeier, OliverKeywords
esports analytics, League of Legends, win probability estimation, match outcome prediction, in-game win probability, win probability added