To take the esports scene to the next level, we introduce PandaSkill, a framework for assessing player performance and skill rating. Traditional rating systems like Elo and TrueSkill often overlook individual contributions and face challenges in professional esports due to limited game data and fragmented competitive scenes. PandaSkill leverages machine learning to estimate in-game player performance from individual player statistics. Each in-game role is modeled independently, ensuring a fair comparison between them. Then, using these performance scores, PandaSkill updates the player skill ratings using the Bayesian framework OpenSkill in a free-for-all setting. In this setting, skill ratings are updated solely based on performance scores rather than game outcomes, hightlighting individual contributions. To address the challenge of isolated rating pools that hinder cross-regional comparisons, PandaSkill introduces a dual-rating system that combines players' regional ratings with a meta-rating representing each region's overall skill level. Applying PandaSkill to five years of professional League of Legends matches worldwide, we show that our method produces skill ratings that better predict game outcomes and align more closely with expert opinions compared to existing methods.

We can evaluate features of problems and their potential solutions well before we can effectively solve them. When considering a game we have never played, for instance, we might infer whether it is likely to be challenging, fair, or fun simply from hearing the game rules, prior to deciding whether to invest time in learning the game or trying to play it well. Many studies of game play have focused on optimality and expertise, characterizing how people and computational models play based on moderate to extensive search and after playing a game dozens (if not thousands or millions) of times. Here, we study how people reason about a range of simple but novel connect-n style board games. We ask people to judge how fair and how fun the games are from very little experience: just thinking about the game for a minute or so, before they have ever actually played with anyone else, and we propose a resource-limited model that captures their judgments using only a small number of partial game simulations and almost no lookahead search.

Modern chess is the culmination of centuries of experience, as well as an evolutionary sequence of rule adjustments from its inception in the 6th century to the modern rules we know today.17 While classical chess still captivates the minds of millions of players worldwide, the game is anything but static. Many variants have been proposed and played over the years by enthusiasts and theorists.8,20 They continue the evolutionary cycle by altering the board, piece placement, or the rules--offering players "something subtle, sparkling, or amusing which cannot be done in ordinary chess."1 Technological progress is the new driver of the evolutionary cycle. Chess engines increase in strength, and players have access to millions of computer games and volumes of opening theory.

In this work, we deal with the problem of rating in sports, where the skills of the players/teams are inferred from the observed outcomes of the games. Our focus is on the online rating algorithms which estimate the skills after each new game by exploiting the probabilistic models of the relationship between the skills and the game outcome. We propose a Bayesian approach which may be seen as an approximate Kalman filter and which is generic in the sense that it can be used with any skills-outcome model and can be applied in the individual-as well as in the group-sports. We show how the well-know algorithms (such as the Elo, the Glicko, and the TrueSkill algorithms) may be seen as instances of the one-fits-all approach we propose. In order to clarify the conditions under which the gains of the Bayesian approach over the simpler solutions can actually materialize, we critically compare the known and the new algorithms by means of numerical examples using the synthetic as well as the empirical data. In this work we are interested in the rating algorithms that can be systematically derived from the probabilistic models which describe i) how the the skills affect the outcomes of the games, as well as ii) how the skills evolve in time, i.e., characterize the skills dynamics. Using the probabilistic models, the forecasting of the game outcomes is naturally derived from the rating.

The AlphaZero algorithm for the learning of strategy games via self-play, which has produced superhuman ability in the games of Go, chess, and shogi, uses a quantitative reward function for game outcomes, requiring the users of the algorithm to explicitly balance different components of the reward against each other, such as the game winner and margin of victory. We present a modification to the AlphaZero algorithm that requires only a total ordering over game outcomes, obviating the need to perform any quantitative balancing of reward components. We demonstrate that this system learns optimal play in a comparable amount of time to AlphaZero on a sample game.

Automation could replace one-third of U.S. jobs within 15 years. Oxford and Yale experts recently predicted that artificial intelligence could outperform humans in a variety of tasks by 2045, ranging from writing novels to performing surgery and driving vehicles. A little human rage would be a natural response to such unsettling news. Artificial intelligence (AI) is bringing us to the precipice of an enormous societal shift. We are collectively worrying about what it will mean for people.

We last heard from DeepMind's dominant gaming AI in October. As opposed to earlier sessions of AlphaGo besting the world's best Go players after the DeepMind team trained it on observations of said humans, the company's Go-playing AI (version AlphaGo Zero) started beating pros after three days of playing against itself with no prior knowledge of the game. On the sentience front, this still qualified as a ways off. To achieve self-training success, the AI had to be limited to a problem in which clear rules limited its actions and clear rules determined the outcome of a game. This week, a new paper (PDF, not yet peer reviewed) details how quickly DeepMind's AI has improved at its self-training in such scenarios. Evolved now to AlphaZero, this latest iteration started from scratch and bested the program that beat the human Go champions after just eight hours of self-training.

Accurately predicting the outcome of sporting events has been a goal for many groups who seek to maximize profit. What makes this challenging is that the outcome of an event can be influenced by many factors that dynamically change across time. Oddsmakers attempt to estimate these factors by using both algorithmic and subjective methods to set the spread. However, it is well-known that both human and algorithmic decision-making can be biased, so this paper explores if oddsmaker biases can be used in an exploitative manner, in order to improve the prediction of NFL game outcomes. Real-world gambling data was used to train and test different predictive models under varying assumptions. The results show that methods that leverage oddsmaker biases in an exploitative manner perform best under the conditions tested in this paper. These findings suggest that leveraging human and algorithmic decision biases in an exploitative manner may be useful for predicting the outcomes of competitive events, and could lead to increased profit for those who have financial interest in the outcomes.

The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6,000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team's position in a competition's final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover we find that, while victory and defeats can be explained by the team's performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data, i.e. excluding the goals scored, exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking (the PC ranking) which is close to the actual ranking, suggesting that a complex systems' view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.

Oxford and Yale experts recently predicted that artificial intelligence could outperform humans in a variety of tasks by 2045, ranging from writing novels to performing surgery and driving vehicles. A little human rage would be a natural response to such unsettling news. Artificial intelligence (AI) is bringing us to the precipice of an enormous societal shift. We are collectively worrying about what it will mean for people. As a doctor, I'm naturally drawn to thinking about AI's impact on the practice of medicine.