Browsing by Author "ijäs, timo"

The topic of this thesis is spatial analytics in competitive gaming and e-sports. The way in which players analyze spatial aspects of gameplay has not been well documented. I study how game, genre and skill level affect the use of spatial analysis in competitive gaming. My aim is also to identify the benefits and challenges of spatial analytics, as well as the need for new spatial analytical tools. Four games of different popular competitive gaming genres were chosen for the study. An online survey was conducted which resulted in a cross-sectional dataset of 2453 responses. It was analyzed using ordinal logistic regression and histogram-based gradient boosting in a cross-validating manner. Open-field answers were summarized using state-of-the-art deep learning methods and analyzed with inductive content analysis. Additionally, experts of each game were interviewed. The results show that the use and understanding of spatial analysis is largely not game- or genre dependent. Players grow spatial skills along with their skill level and start using more complex spatial analytical methods more frequently as their skill level rises. It is exceedingly rare that expert players do not analyze spatial aspects of their gameplay. There is a need for different kinds of spatial analytics tools in all competitive games, and the benefits of advanced tools to a player and the community can be large. However, the tools need to be highly contextualized, fine-tuned for each game specifically, and tailored to the players’ needs. Creating new tools for spatial analytics is something useful for competitive gaming as a whole. The inclusion of more detailed spatial analytical tools can lead to a new era of competitive gaming. E-sports is a rapidly growing phenomenon, and the analytics that support its growth should follow.