How does the difficulty of a task affect people's enjoyment and engagement? Intrinsic motivation and flow theories posit a 'goldilocks' optimum where task difficulty matches performer skill, yet current work is confounded by questionable measurement practices and lacks scalable methods to manipulate objective difficulty-skill ratios. We developed a two-player tactical game test suite with an artificial intelligence (AI)-controlled opponent that uses a variant of the Monte Carlo Tree Search algorithm to precisely manipulate difficulty-skill ratios. A pre-registered study (n = 311) showed that our AI produced targeted difficulty-skill ratios without participants noticing the manipulation, yet different ratios had no significant impact on enjoyment or engagement. This indicates that difficulty-skill balance does not always affect engagement and enjoyment, but that games with AI-controlled difficulty provide a useful paradigm for rigorous future work on this issue.

Esports are competitive videogames watched by audiences. Most esports generate detailed data for each match that are publicly available. Esports analytics research is focused on predicting match outcomes. Previous research has emphasized prematch prediction and used data from amateur games, which are more easily available than those from professional level. However, the commercial value of win prediction exists at the professional level. Furthermore, predicting real-time data is unexplored, as is its potential for informing audiences. Here, we present the first comprehensive case study on live win prediction in a professional esport. We provide a literature review for win prediction in a multiplayer online battle arena (MOBA) esport. This article evaluates the first professional-level prediction models for live DotA 2 matches, one of the most popular MOBA games, and trials it at a major international esports tournament. Using standard machine learning models, feature engineering and optimization, our model is up to 85% accurate after 5 min of gameplay. Our analyses highlight the need for algorithm evaluation and optimization. Finally, we present implications for the esports/game analytics domains, describe commercial opportunities and practical challenges, and propose a set of evaluation criteria for research on esports win prediction.

Within limited-input language classrooms, understanding the effect of distribution of practice (spacing between practice) on learning is critical, yet evidence is conflicting and of limited relevance for young learners. For second language (L2) grammar learning, some studies reveal advantages for spacing of 7 days or more, but others for shorter spacing. Further, little is known about the role of cognitive individual differences (e.g., language analytic ability; LAA) in mediating practice distribution effects for L2 grammatical knowledge development and retention. To address this gap, this classroom-based study investigated whether distribution of practice and LAA moderated the effectiveness of explicit, input-based grammar instruction for young first language (L1) English learners of French (aged 8 to 11). The study revealed minimal differences between longer (7-day) versus shorter (3.5-day) spacing of practice for learning a French verb inflection subsystem, at either posttest or delayed posttest. Minimal group-level gains and substantial within-group variation in performance at posttests were observed. Accuracy of practice during training and LAA were significantly associated with posttest performance under both practice schedules. These findings indicated that within an ecologically valid classroom context, differences in distribution of practice had limited impact on learner performance on our tests; rather, individual learner differences were more critical in moderating learning. This highlights the importance of considering individual learner differences in the development of resources and the potential of digital tools for dynamically adapting instruction to suit individuals.

Esports - video games played competitively that are broadcast to large audiences - are a rapidly growing new form of mainstream entertainment. Esports borrow from traditional TV, but are a qualitatively different genre, due to the high flexibility of content capture and availability of detailed gameplay data. Indeed, in esports, there is access to both real-time and historical data about any action taken in the virtual world. This aspect motivates the research presented here, the question asked being: can the information buried deep in such data, unavailable to the human eye, be unlocked and used to improve the live broadcast compilations of the events? In this paper, we present a large-scale case study of a production tool called Echo, which we developed in close collaboration with leading industry stakeholders. Echo uses live and historic match data to detect extraordinary player performances in the popular esport Dota 2, and dynamically translates interesting data points into audience-facing graphics. Echo was deployed at one of the largest yearly Dota 2 tournaments, which was watched by 25 million people. An analysis of 40 hours of video, over 46,000 live chat messages, and feedback of 98 audience members showed that Echo measurably affected the range and quality of storytelling, increased audience engagement, and invoked rich emotional response among viewers.

In this paper, we analyze the gameplay data of three popular customizable card games where players build decks prior to gameplay. We analyze the data from a player engagement perspective, how the business model affects players, how players influence the business model and provide strategic insights for players themselves. Sifa et al. found a lack of crass-game analytics, whereas Marchand and Hennig-Thurau identified a lack of understanding of how a game's business model and strategies affect players. We address both issues. The three games have similar business models but differ in one aspect: the distribution model for the cards used in the game. Our longitudinal analysis highlights this variation's impact. A uniform distribution creates a spread of decks with slowly emerging trends while a random distribution creates stripes of deck building activity that switch suddenly each update. Our method is simple, easily understandable, independent of the specific game's structure, and able to compare multiple games. It is applicable to games that release updates and enables comparison across games. Optimizing a game's updates strategy is the key, as it affects player engagement and retention, which directly influence businesses' revenues and profitability in the $95 billion global games market.

Esports has emerged as a popular genre for players as well as spectators, supporting a global entertainment industry. Esports analytics has evolved to address the requirement for data-driven feedback, and is focused on cyber-athlete evaluation, strategy and prediction. Towards the latter, previous work has used match data from a variety of player ranks from hobbyist to professional players. However, professional players have been shown to behave differently than lower ranked players. Given the comparatively limited supply of professional data, a key question is thus whether mixed-rank match datasets can be used to create data-driven models which predict winners in professional matches and provide a simple in-game statistic for viewers and broadcasters. Here we show that, although there is a slightly reduced accuracy, mixed-rank datasets can be used to predict the outcome of professional matches, with suitably optimized configurations.

Monte Carlo Tree Search (MCTS) has become a popular solution for controlling non-player characters. Its use has repeatedly been shown to be capable of creating strong game playing opponents. However, the emergent playstyle of agents using MCTS is not necessarily human-like, believable or enjoyable. AI Factory Spades, currently the top rated Spades game in the Google Play store, uses a variant of MCTS to control non-player characters. In collaboration with the developers, we collected gameplay data from 27,592 games and showed in a previous study that the playstyle of human players significantly differed from that of the non-player characters. This paper presents a method of biasing MCTS using human gameplay data to create Spades playing agents that emulate human play whilst maintaining a strong, competitive performance. The methods of player modelling and biasing MCTS presented in this study are generally applicable to digital games with discrete actions.

As part of their design, card games often include information that is hidden from opponents and represents a strategic advantage if discovered. A player that can discover this information will be able to alter their strategy based on the nature of that information, and therefore become a more competent opponent. In this paper, we employ association rule mining techniques for predicting item multisets, and show them to be effective in predicting the content of Netrunner decks. We then apply different modifications based on heuristic knowledge of the Netrunner game, and show the effectiveness of techniques which consider this knowledge during rule generation and prediction.

The final step in the Monte Carlo Tree Search algorithm is to select the action to play from the root level of the tree. Experimentation on modifying the selection mechanism has been somewhat limited to date, particularly with respect to consider aspects other than playing strength. This paper investigates the modification of selection mechanism as an attempt to produce a more entertaining opponent in the strategic card game Lords of War. These selection mechanisms are played against our most effective Information Set MCTS agent, and we investigate their performance in terms of measures of performance and complexity. An interesting side effect is that one of the action selection mechanisms results in a significant improvement in ISMCTS play strength. We also experiment with online tuning of configuration parameters in an attempt to create an agent with dynamically scaling play strength.

Process parallelization is more important than ever, as most modern hardware contains multiple processors and advanced multi-threading capability. This paper presents an analysis of the parallel behaviour of Information Set Monte Carlo Tree Search and the Upper Confidence Bounds for Trees (UCT) variant of MCTS, and certain parallelization techniques (specifically Tree Parallelization) have different effects upon ISM-CTS and Plain UCT. The paper presents a study of the relative effectiveness of different types of parallelization, including Root, Tree, Tree with Virtual Loss, and Leaf.

Move pruning is a technique used in game tree search which incorporates heuristic knowledge to reduce the number of moves under consideration from a particular game state. This paper investigates Heuristic Move Pruning on the strategic card game Lords of War. We use heuristics to guide our pruning and experiment with different techniques of applying pruning and their relative effectiveness. We also present a technique of artificially rolling forward a game state in an attempt to more accurately determine which moves are appropriate to prune from the decision tree. We demonstrate that heuristic move pruning is effective in Lords of War, and also that artificially rolling forward the game state can increase the effectiveness of heuristic move pruning.