Explainable e-sports win prediction through Machine Learning classification in streaming

The increasing number of spectators and players in e-sports, along with the development of optimized communication solutions and cloud computing technology, has motivated the constant growth of the online game industry. Even though Artificial Intelligence-based solutions for e-sports analytics are traditionally defined as extracting meaningful patterns from related data and visualizing them to enhance decision-making, most of the effort in professional winning prediction has been focused on the classification aspect from a batch perspective, also leaving aside the visualization techniques. Consequently, this work contributes to an explainable win prediction classification solution in streaming in which input data is controlled over several sliding windows to reflect relevant game changes. Experimental results attained an accuracy higher than 90 %, surpassing the performance of competing solutions in the literature. Ultimately, our system can be leveraged by ranking and recommender systems for informed decision-making, thanks to the explainability module, which fosters trust in the outcome predictions. Keywords: Artificial Intelligence; e-sports; explainability; Machine Learning; real-time data analytics; win prediction 1. Introduction Electronic sports (i.e., e-sports) are one of the world's most international and popular sportive events, involving millions of users [1]. The increasing number of spectators and players in e-sports, along with the development of optimized communication solutions and cloud computing technology, has motivated the constant growth of the online game industry [2] and the increasing popularity of Artificial Intelligence- (ai) based solutions for e-sports analytics [3]. Notably, the commercial relevance of professional winning prediction is remarkable. In this line, note that this is the most watched part of the game commentator show [4]. Most e-sports, particularly regarding the multiplayer online battle arena (moba) e-sports (e.g., Counter-Strike: Global Offensive - cs: go, Defense of the Ancients 2 - dota 2, Heroes of Newerth - hon, Honor of Kings - hok, League of Legends - lol, Tactical Troops: Anthracite Shift - tt: as) attract millions of players and thus, produce a publicly available stream of data regarding matches and competitions.