The collective performance displayed by groups or teams, whether in solving complex problems or excelling in esports competitions, hinges on their coordination dynamics. While explicit coordination (e.g., verbal commands) and its affects on collective outcomes have been studied extensively, implicit coordination, especially in dynamic, fast-paced environments have been under investigated. In this study, we examine the competitive esportDota 2(D2) as a setting to explore within-team implicit coordination, which we model as player avatar movement synchrony. Utilizing the cluster phase method, we analyze spatio-temporal patterns of player movements to quantify implicit movement coordination. We observe a negative linear relationship between team movement synchrony and team performance in rank for D2 competitions across two tournaments. While some research suggests stronger coordination leads to favorable outcomes, we leverage our findings to discuss the complexity of team coordination, showcasing a delicate balance between specialization of individual team members and collective action. This study not only extends complex systems techniques used in physical sports to the rapidly evolving esports arena, but also invites further exploration into the multidimensional nature of coordination in team-based activities.

Traditionally, the relative strength of a chess player within a competitive pool is identified by a rating number. In order to reach a fair rating that best represents their level of play, chess players are required to play numerous games against various opponents within that pool. However, intuitively, experienced chess players are capable of extracting a rough estimate of a player’s strength by looking at the moves they made in a single game. How accurately could a machine learning model based on a large dataset of chess games predict player ratings from a single game, and what would these predictions depend on? This paper presents an attempt to identify, encode and model chess gameplay features in order to predict a player’s rating from a single game played. If successful, such a model could be employed to attach a fair initial rating to a new player within a pool before any games are played. We use an extensive dataset of chess games downloaded from a popular online chess platform, from which we extract a set of 30 features which are used to model and ultimately predict players’ ratings. Our findings show that we are capable of predicting the rating bracket of a player with 79.3% accuracy when considering the extreme ends of the dataset (lowest vs. highest rated players), while the accuracy consistently drops as we increase the respective bracket width. We discovered that the most important features of our predictive models are both theory-and engine-related; most importantly, the features that we have extracted lead to explainable, quantifiable predictions of chess player strength.

Games are designed to elicit strong emotions during game play, especially when players are competing against each other. Artificial Intelligence applied to predict a player’s emotions has mainly been tested on single-player experiences in low-stakes settings and short-term interactions. How do players experience and manifest affect in high-stakes competitions, and which modalities can capture this? This paper reports a first experiment in this line of research, using a competition of the video game Hearthstone where both competing players’ game play and facial expressions were recorded over the course of the entire match which could span up to 41 minutes. Using two experts’ annotations of tension using a continuous video affect annotation tool, we attempt to predict tension from the webcam footage of the players alone. Treating both the input and the tension output in a relative fashion, our best models reach 66.3% average accuracy (up to 79.2% at the best fold) in the challenging leave-one-participant out cross-validation task. This initial experiment shows a way forward for affect annotation in games “in the wild” in high-stakes, real-world competitive settings.

In this study, we extracted facial action units (AUs) data during a Hearthstone tournament to investigate behavioural differences between expert, intermediate, and novice players. Our aim was to obtain insights into the nature of expertise and how it may be tracked using non-invasive methods such as AUs. These insights may shed light on the endogenous responses in the player and at the same time may provide information to the opponents during a competition. Our results show that player expertise may be characterised by specific patterns in facial expressions. More specifically, AU17 (chin raiser), AU25 (lips apart), and AU26 (jaws drop) intensity responses during gameplay may vary according to players’ expertise. Such results were obtained by training a random forest classifier to test whether we can use these three AUs alone to accurately detect players’ expertise. The classifier reached 0.75 accuracy on 5-fold cross-validation, after balancing the class weights, and 0.85 after having applied the Synthetic Minority Over-sampling Technique (SMOTE) function. These results suggest that AUs can be effectively used to discriminate different levels of expertise in competitive video game players.

In this study, we analyzed the blinking behavior of players in a video game tournament. We aimed to test whether spontaneous blink patterns differ across levels of expertise. We used blink rate (blinks/m), blink duration, and general eyelid movements represented by features extracted from the Eye Aspect Ratio (EAR) to train a machine learning classifier to discriminate between different levels of expertise. Classifier performance was highly influenced by features such as the mean, standard deviation, and median EAR. Moreover, further analysis suggests that the blink rate is likely to increase with the level of expertise. We speculate this may be indicative of a reduction in cognitive load and lowered stress of expert players. In general, our results suggest that EAR and blink patterns can be used to identify different levels of expertise of video game players.

Esports have evolved into a major form of entertainment, drawing hundreds of millions of viewers to its online competitive broadcasts. Using Esports telemetry data to predict the outcome of a match is a well-researched topic, but micropredictions of specific in-game events are explored only sparingly. How accurately can we predict specific in-game events within a limited time window, and how can these predictions be used in a live broadcast? This research aims at predicting in-game deaths using telemetry data in Counter-Strike: Global offensive (CS:GO). We establish a data processing pipeline to acquire and re-structure raw in-game data and propose a set 36 features which will ultimately be used to predict in-game deaths within a three second window. Three neural network models are compared, namely convolutional (CNN), recurrent (RNN) and long short-term memory (LSTM). Our results show that the LSTM network has the best predictive accuracy (F1 0.38) when prompted, for all 10 players of a competitive game of CS:GO. The predictions are most influenced by features related to a player’s average in-game death count, health points, enemies in range and equipment value. Our model enables real-time micropredictions of deaths in CS:GO, and may be leveraged by Esports commentators and game observers to direct their focus on critical in-game events during a live competitive broadcast.

This paper investigates the development of an Artificial Intelligence (AI) agent which plays the voting phase of the board game Dixit. Given a set of open cards and a lexical “hint” provided by a player, our algorithm aims to predict which card the hint originally refers to. The AI agent is developed using Machine Learning (ML) algorithms for Natural Language Processing (NLP). The AI agent is equipped with models that explore data of human-played games and retrieves information from the internet to deal with any shortage of information. We show that the Dixit AI agent we developed is more accurate than the average human player in finding the card which corresponds to a hint.

In this study, we used recordings of players’ facial expressions that are captured during competitive Hearthstone games to analyse the correlation between in-game player affective responses and subjective post-game self-reports. With this, we aimed to examine whether eye gaze, head pose and emotions gathered as objective data from face recordings would be associated with subjective experiences of players which were collected in the form of a post-game survey. Data was collected during a live offline Hearthstone competition, which involved a total of 17 players and 31 matches played. Correlation analyses between in-game and post-game variables show that players’ facial expressions and eye gaze measurements are associated with both players’ attention to the opponent and their mood influenced by the opponent. In future research, these results may be used to implement predictive player models.

In this paper, we study the effect of time pressure on player behaviour during a dilemma-based crisis management game. We employ in-game action tracking, physiological sensor data and self-reporting in order to create multi-modal predictive models of player stress responses during a crisis management scenario. We were able to predict the experimental condition (time pressure vs. no time pressure) with 84.5% accuracy, using a game-only feature set. However, lower accuracy was observed when physiological sensor data was used for the same task. The method presented in this paper can be employed in crisis management training, aiming at assessing players’ responses to stressful conditions and manipulating player stress levels to provide personalised training scenarios.

With the constant rise in popularity of competitive video gaming (also known as Esports), Esports analytics has been a field of growing scientific interest in the recent years. Studies discussing player behaviour, skill learning and team performance have been conducted through Multiplayer Online Battle Arena games such as League of Legends. In this paper, we propose a multi-modal approach towards stress response modeling in competitive LoL games. We collect wearable physiological sensor data, mouse & keyboard logs and in-game data in order to study the relationship between player stress responses and in-game behaviour. We discuss the design criteria and propose future studies using the collected dataset.

In this paper, we propose a framework for personalised crisis management training through the use of an applied game. The framework particularly focuses on ubiquitously assessing and manipulating player stress levels during training, and evaluating player performance by providing personalised feedback. To achieve these goals, the framework leverages techniques for multi-modal player modeling through physiological sensors, in-game events and self-report data. Specifically, the present paper (1) discusses design decisions for the personalised crisis management training framework, and (2) presents the game prototype with which user-studies will be performed. Presently, the game prototype is being developed in close collaboration with actual crisis management experts.