Skill Acquisition and Player Performance in Video Games and Esports

Video games have become one of the most popular entertainment products in the world during the past decade. Backed by a profitable industry, video games entice millions of players to play for hundreds and sometimes thousands of hours. Many of these players seek to acquire the necessary skills to excel in their favourite games, be it as a personal challenge in the context of single player games or to participate in structured competition for competitive multiplayer games also known as esports. However, casual players and professionals alike show a distinct lack of any systematic approaches to training in the context of their games and resort to brute forcing adaption through playing for as many hours as possible every day. This behavioural pattern involves sitting and intense computer use for extended periods of time which can lead to adverse mental and physical health consequences such as disturbed sleep patterns, overuse injuries in the hands and wrists, and back pain. To address this problem, this dissertation compiles necessary knowledge about the multifaceted nature of skill and skill acquisition while distinguishing between perceptual learning, response selection, attentional skill, motor skill, and cognitive skill as distinct dimensions that require different training methods. This knowledge functions as a theoretical foundation for structured training in video games. To apply this knowledge, this dissertation introduces the Player Challenge Model, which provides a step by step framework for the analysis of video games for the sake of extracting skill requirements based on the mechanics a game employs to facilitate the selection of suitable training methods. Two user studies on the effects of long play sessions of League of Legends conducted in this thesis emphasize the importance of using such a framework as both studies show detrimental effects on performance and win rates with indications for mental fatigue, which in turn negatively impacts cognitive functions associated with learning. Alternative methods for video game training are explored as part of the Player Challenge Model and put to the test in two additional user studies, one focused on distributed training in an ecologically valid esports task and the other exploring the use of a fully integrated digital tool comparing immediate and delayed feedback in a speedrunning task. The results demonstrate the potential of exploring new training approaches for video games as well as the associated difficulties. Together with extensive literature work and expert interviews conducted with professional fighting game players that confirm the lack of systematic training approaches even in those players who succeed in their chosen games, this thesis provides the necessary ground work for further development of structured training systems in video games and emphasize the potential utility of their use.