Despite the popularity of Overwatch, many new players-who join the game unsure how to compete with the game’s veterans-feel overwhelmed with the vast knowledge required to properly play at higher skill levels. Since its debut in May 2016, Overwatch has quickly become a popular team-based online video game. In conclusion, the effective use of the army, based on optimizing the ratio between units lost and units killed, may be significant in predicting the game outcome. It seems evident that winner optimized interaction with an opponent by keeping his/her own army intact while inflicting damage to the opponent’s army or economy. The performance indicators which showed the strongest effect in predicting the game outcome were “minerals lost army”, “minerals killed army”, “minerals used current army”, and “minerals killed economy”. The model was able to discriminate the game outcome (won, lost) with an out-of-sample accuracy of 0.728 ± 0.021. Logistic regression with 5-fold cross-validation was performed to predict the game outcome. In total, 3719 game records and 9 performance indicators were obtained after applying the inclusion criteria. Each game record contained data for both the winner and the loser. The distribution of analyzed players concerning the preferred in-game race was as follows: “Protoss” (n = 3), “Zerg” (n = 1), “Terran” (n = 1). The aim of this study was to investigate performance indicators from in-game data to predict the outcome of the matches in StarCraft II: Legacy of The Void.ĭata from 6509 games (game records) provided by 5 players at the level of Master or GrandMaster were used. Insights on gameplay and underlying processes may push the development of new and optimal practice methods. Esports offer a unique opportunity to conduct human performance studies, as they use modern hardware and software as an operation platform.
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