DOTA 2 match prediction through deep learning team fight models

Esports are complex computer games that are played competitively. DOTA 2 is one of the most popular esports titles worldwide. Commentators, audiences, and players face tremendous challenges to keep up with events happening during live matches due to a rapidly evolving gameplay across a large virtual arena. This complexity leads to the question of whether esports analytics could detect important events and their subsequent impact on the match. One such important event is team fights, which can often determine the outcome of a match. Despite their significance across strategy, gameplay, and audience experience, team fights remain relatively unexplored in the literature. Their role and potential to support match prediction models are not well understood. This paper presents a novel definition of team fights in DOTA 2 and proposes an algorithm to extract and quantity them for use in match prediction.