Creating Testing Environments for AI Research in Sports
Developing AI technology that can assist humans in making real-time decisions in dynamic, multiagent environments like sports is a challenging task. Sports provide an exciting opportunity for AI research, as the availability of sports data is increasing rapidly. This data includes detailed information such as event streams, player positions, and on-body sensors. However, the field of sports analytics has only recently started utilizing machine learning and AI to aid decision-making in sports.
In a recent collaboration with Liverpool Football Club, we published a paper envisioning the future of sports analytics. We propose using a combination of statistical learning, video understanding, and game theory to advance the field. We focus specifically on football as a microcosm for studying AI research.
Football Analytics and AI
Football has been relatively slow in collecting large sets of data for scientific analytics compared to other sports. This is due to the game’s uncontrollable settings and the emphasis on human specialists. However, football analytics is uniquely suited for AI techniques that involve computer vision, statistical learning, and game theory.
Game theory plays a significant role in understanding players’ behavioral strategies in sports. Many scenarios in football can be modeled as zero-sum games, such as penalty kicks. We use game-theoretic analysis and player representations called Player Vectors to analyze kicking strategies. The results show statistically distinct shooting strategies for different groups of players. These insights can help goalkeepers adapt their defense strategies against different types of players.
Statistical learning, particularly representation learning, has yet to be fully utilized in sports analytics. By combining statistical learning with game theory, we can gain deeper insights into player behavior and decision-making. For example, we can study ‘ghosting’, which involves analyzing how players should have acted in hindsight. This analysis can help predict the implications of tactical changes or player injuries on team performance.
Computer vision is a promising avenue for advancing sports analytics research. By detecting events from video, we can make videos searchable and generate automatic highlights. This opens up a wide range of applications in sports analytics.
In conclusion, AI has the potential to revolutionize sports analytics by assisting decision-makers in real-time. By combining game theory, statistical learning, and computer vision, we can gain valuable insights into player behavior and optimize team strategies. Football serves as a useful testing ground for advancing AI research in sports.