A generic data representation for predicting player behaviours

A common use of predictive models in game analytics is to predict the behaviours of players so that pre-emptive measures can be taken before they make undesired decisions. A standard data pre-processing step in predictive modelling includes both data representation and category definition. Data representation extracts features from the raw dataset to represent the whole dataset. Much research has been done towards predicting important player behaviours with game-specific data representations. Some of the resulting efforts have achieved competitive performance; however, due to the game-specific data representations they apply, game companies need to spend extra efforts to reuse the proposed methods in more than one products. This work proposes an event-frequency-based data representation that is generally applicable to games. This method of data representation relies only on counts of in-game events instead of prior knowledge of the game. To verify the generality and performance of this data-representation, it was applied to three different genres of games for predicting player first-purchasing, disengagement and churn behaviours. Experiments show that this data representation method can provide a competitive performance across different games. Category definition is another essential component of classification problems. As labelling method that relies on some specific conditions to distribute players into classes can often lead to imbalanced classification problems, this work applied two commonly used approaches, i.e., random undersampling and Synthetic Minority Over-Sampling Technique (SMOTE), for rebalancing the imbalanced tasks. Results suggested that undersampling is able to provide better performance in the cases where the quantity of data is sufficient whereas the SMOTE has more chances when the dataset is too small to be balanced with the undersampling approach. Besides, this work also proposes a new category-definition method which can maintain a distribution of the resultant classes that is closer to balanced. In addition, the parameters used in this method can also be used to gain insight into the health of the game. Preliminary experimental results show that this method of category definition is able to improve the balance of the class distribution when it is applied to different games and provide significantly better performance than random classifiers

Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE

In non-contractual freemium and sharing economy settings, a small share of users often drives the largest part of revenue for firms and co-finances the free provision of the product or service to a large number of users. Successfully retaining and upselling such high-value users can be crucial to firms\u27 survival. Predictions of customers\u27 Lifetime Value (LTV) are a much used tool to identify high-value users and inform marketing initiatives. This paper frames the related prediction problem and applies a number of common machine learning methods for the prediction of individual-level LTV. As only a small subset of users ever makes a purchase, data are highly imbalanced. The study therefore combines said methods with synthetic minority oversampling (SMOTE) in an attempt to achieve better prediction performance. Results indicate that data augmentation with SMOTE improves prediction performance for premium and high-value users, especially when used in combination with deep neural networks

Bayesian Inference for Predicting the Monetization Percentage in Free-to-Play Games

Free-to-play has become one of the most popular monetization models, and as a consequence game developers need to get the players to purchase in the game instead of getting players to buy the game. Game analytics and player monetization prediction are important parts in estimating the profitability of a free-to-play game. In this paper, we concentrate on predicting the fraction of monetizing players among all players. Our method is based on a survival analysis mixture cure model, and can be applied to unlabeled data collected from any free-to-play game. We formulate a statistical model and use the Expectation Maximization algorithm to solve the latent monetization percentage and the monetization rate. The original method is modified by using Bayesian inference, and the results of the versions are compared. The method can be applied as a preliminary profitability study in situations where there is no extensive historical game data available, such as game and business development scenarios that need to utilize real time analytics. Index Terms—Bayesian Inference, Free-to-play, Monetization, Survival Analysis</p

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Learning Exergame Enjoyment

This Major Qualifying Project used data collection on how study participants played Pokémon Go to perform analysis and classify the enjoyment of Exergames. Using an Android app developed for this project, data about how users played the game was gathered and then uploaded for analysis. Descriptive statistics and analyses were performed using Plot.ly, while classification and classification result analysis were performed with MATLAB

Can videogames be addicting? An investigation into the specific game features and personal characteristics associated with problematic videogame playing

The number of individuals who play videogames has increased dramatically in recent years. Unsurprisingly, the frequency with which patients seek psychotherapeutic services to help cope with problematic videogame playing (PVGP) behaviors has also risen. Thus, explorations into the specific characteristics of PVGP are essential now more than ever before. However, the current state of the literature primarily relies on comparisons between PVGP and pathological gambling, utilizing modified measures of the latter to assess the former. To date, no studies have attempted to adapt the diagnostic criteria for substance use disorder (SUD) in an effort to understand PVGP within the context of addiction. Further, few studies have explored the specific game characteristics and individual factors that contribute to the presence of PVGP. The current study sought to address these questions by adapting the SUD criteria to address videogame-related behavior via a measure labeled as the Videogame Addiction Scale (VGAS). Comparisons of the psychometrics and criterion validity of the VGAS and leading measures of PVGP suggested the former was superior. Further, results indicated that higher levels of addiction were present in players who prefer the MMORPG and Shooter genres over all other types of games, with the former yielding significantly higher VGAS scores than the latter. Further, many of the structural characteristics of videogames were considered to be more enjoyable, important, and associated with longer playtimes for individuals with higher “addiction” scores than their low scoring counterparts. Lastly, a model of videogame addiction was generated that aligns with the current literature on substance use disorders. Specifically, impulsivity, maladaptive coping, weekly playtime, and particular structural characteristics all seem to relate to videogame addiction