Advancements in Machine Learning and AI in Sport
The rapid expansion of technology in sport has led to an explosion of data collection and the range of attributes measured and recorded. This influx of data offers both challenges and opportunities, prompting critical questions for coaches and athletes: How can training be optimized, competitiveness enhanced, and injuries avoided? Traditional statistical methods have often reached their limits, making room for machine learning (ML) and artificial intelligence (AI) to take the forefront in analyzing sports data. Early Promises of AI in Sport In 1995, Lapham and Bartlett highlighted AI’s potential to support decision-making in sports, speeding up analyses and freeing experts’ time and resources. Since then, ML has been increasingly used to build decision support systems. ML applications in sport offer numerous benefits: automating data collection, processing data into meaningful information, identifying critical performance and health information, and assisting in complex decision-making processes. Practical Applications of ML in Sport Elite coaches and athletes often rely on their experience and intuition for decision-making. However, the complexity and number of interacting factors can make some decisions challenging. With the right data, ML methods can create models that support these decisions. Data Collection and Processing Innovations in data collection and processing are crucial. For instance, Hosp et al. developed a deep learning tool to classify the perceptual performance expertise of soccer goalkeepers. Van Dijk et al. improved data processing from inertial measurement units, providing more accurate body position information in wheelchair sports. Additionally, Schmid et al. created a tool to identify tactical patterns in American Football, using these annotations for defensive simulations. Evaluating Performance New measures to evaluate individual and team performance are a significant focus. Anzer and Bauer introduced an enhanced method for calculating expected goals in soccer, a key performance indicator. The “zones of control” concept, vital for analyzing tactical performance in sports like soccer, was advanced by Martens et al. They improved methods to calculate space control, aiding offensive performance analysis. Dick et al. explored ball possession phases in soccer, presenting new performance indicators to assess and coach player performance. Predictive Models ML has also been used to predict athletic performance. Olthof et al. studied the relationship between biomechanical load and performance in basketball, finding that game load predicts technical performance and training load predicts game load. Kholkine et al. developed a model to help coaches identify key adversaries in 1-day road cycling races and adapt strategies based on patterns predicting future race winners. The Value of Sport to ML Each article in this research topic represents significant advancements in ML applications in sport, improving data processing, identifying critical performance indicators, and creating predictive models for complex decision-making. Sport provides high-quality, rich datasets on various human behaviors constrained by clear rules and supported by trained experts, offering unique opportunities for supervised ML. Future Directions The continued development of ML in sport relies on educating students and practitioners about its benefits and applications. Collaboration between sport scientists and data scientists is crucial for advancing the field. Leaders must drive innovation and provide ethical guidance to avoid risky applications. The deployment of ML solutions in the field will collect data, conduct preliminary analyses, and make recommendations, continually improving sports performance and decision-making. The integration of ML and AI in sport is transforming data collection, analysis, and decision-making. By leveraging these technologies, the sports industry can enhance training, performance and injury prevention. The future of ML in sport looks promising, with ongoing collaborations and innovations driving the field forward. The Sportz Planet Desk,Atharva Shetye
