Behavioral game analytics has predominantly been confined to work on single games, which means that the cross-game applicability of current knowledge remains largely unknown. Here four experiments are presented focusing on the relationship between game ownership, time invested in playing games, and the players themselves, across more than 3000 games distributed by the Steam platform and over 6 million players, covering a total playtime of over 5 billion hours. Experiments are targeted at uncovering high-level patterns in the behavior of players focusing on playtime, using frequent itemset mining on game ownership, cluster analysis to develop playtime-dependent player profiles, correlation between user game rankings and, review scores, playtime and game ownership, as well as cluster analysis on Steam games. Within the context of playtime, the analyses presented provide unique insights into the behavior of game players as they occur across games, for example in how players distribute their time across games.
Chen, Zhengxing (Northeastern University) | Nasr, Magy Seif El (Northeastern University) | Canossa, Alessandro (Northeastern University) | Badler, Jeremy (Northeastern University) | Tignor, Stefanie (Northeastern University) | Colvin, Randy (Northeastern University)
There has been much work on player modeling using game behavioral data collected. Many of the previous research projects that targeted this goal used aggregate game statistics as features to develop behavior models using both statistical and machine learning techniques. While existing methods have already led to interesting findings, we suspect that aggregated features discard valuable information such as temporal or sequential patterns, which may be important in deciphering information about decisionmaking, problem solving, or individual differences. Such sequential information is critical to analyze player behaviors especially in role-playing games (RPG) where players can face ample choices, experience different contexts, behave freely with individual propensities but possibly end up with similar aggregated statistics (e.g., levels, time spent). In this paper we intend to develop and apply a modeling technique that takes into consideration sequential patters to decipher individual differences in playing a Role Playing Game (RPG) game. Using an RPG with multiple affordances, we designed an experiment collecting granular in-game behaviors of 64 players. Using closed sequential pattern mining and logistic regression, we developed a model that uses gameplay action sequences to predict the real world characteristics, including gender, game play expertise and five personality traits (as defined by psychology). The results show that game expertise is a dominant factor that impacts in-game behaviors. The contribution of this paper is the algorithms we developed combined with a validation procedure to determine the reliability and validity of the results and the results themselves.
Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy's The Division (Ubisoft, 2016). On the other end we ask them to report their levels of competence, autonomy, relatedness and presence using the in-house designed Ubisoft Perceived Experience Questionnaire. After processing the survey responses in an ordinal fashion we employ preference learning methods, based on support vector machines, to infer the mapping between gameplay and the four motivation factors. Our key findings suggest that gameplay features are strong predictors of player motivation as the obtained models reach accuracies of near certainty, in particular, from 93% up to 97% on unseen players.
With game play data, empirical approaches to clustering are typically based solely on game outcomes, e.g. kills, deaths, and score for each player. In this paper, we investigate a method for clustering players based on how a player’s choices relate to outcomes, or equivalently the latent player styles exhibited by players. Our approach is based on a Bayesian semi-parametric clustering method which has several advantages: the number of clusters do not need to be specified a priori; the technique can work with a very compact representation of each match (e.g. consisting primarily of indicator variables for player choices); a player can belong to multiple clusters and hence can have a hybrid style; and the resulting clusterings often have a straight-forward interpretation. To demonstrate the approach, we apply our method to multiplayer match logs from Battlefield 3 consisting of over 1200 players and 500,000 matches.
Lora, Diana (Universidad Complutense de Madrid) | Sánchez-Ruiz, Antonio A. (Universidad Complutense de Madrid) | González-Calero, Pedro A. (Universidad Complutense de Madrid) | Gómez-Martín, Marco A. (Universidad Complutense de Madrid)
A game fun to play is the one that provides challenges to the players corresponding to their skills. Most of the games have different preconfigured difficulty levels, but they do not adjust the difficulty dynamically to the player skill. In this work, we explore the idea of creating clusters from previous game traces to capture different playing styles in Tetris and then use those clusters to decide how much help the system should provide to new players giving them good Tetris pieces. In our experiments players report improvements in terms of game experience.