AI in Sports Analytics and Performance Optimization
1. Introduction to AI in Sports
In this chapter, we address the use of artificial intelligence in sports from two perspectives. The first is the use of AI in the analysis of a sport to improve strategy and outcome. The second is the use of AI in the training and performance optimization of athletes. As we shall see, these two aspects are intertwined. Better insight into a sport can lead to better performance of athletes and vice versa. The rest of this section is organized as follows. We begin by introducing the reader to the concept of AI. We then provide an overview of the chapters. Finally, we address the use of AI in the analysis of a sport.
Sporting contests have captured the imagination of human beings since time immemorial. Sport is a medium through which spectators live the intense emotions of victory and defeat. For the athletes, it is a hard field where they push the limits of their bodies and minds. The appeal of sport is such that today it is a multi-billion dollar industry. At the heart of this industry are the athletes whose performance is scrutinized in every possible way. Coaches and trainers use a variety of techniques to help athletes realize their full potential. Over the centuries, sport has seen the use of a variety of tools and techniques, both legal and illegal, to enhance performance. Today, the use of technology has opened new frontiers in the quest for the optimal performance of athletes. In particular, the use of data analytics, machine learning, and artificial intelligence are proving to be game-changers — both in terms of the analysis of a sport and the performance of athletes.
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1.1. Definition and Scope of AI in Sports
Sports analytics is a dominant application domain of AI in sports. It refers to the process of using AI technologies to analyze sports data (e.g., player, team, and game-related data) and generate insights that can inform sports-related decision-making in areas such as talent scouting, game strategy, and performance optimization. These sports data can either be structured (e.g., game score, player statistics) or unstructured (e.g., sports video, social media text). Structured sports data are usually analyzed using machine learning models, such as classification, regression, clustering, and deep learning models, while unstructured sports data are often analyzed using computer vision and natural language processing models. Since sports data are diverse and complex, a wide variety of AI methods can be applied to perform sports analytics across different sports and at various levels of analysis. Ultimately, sports analytics has the overarching goal of increasing the probability of favorable sports outcomes through data-driven decision-making.
Artificial intelligence (AI) in sports is a sub-field of AI that focuses on the use of AI technologies such as machine learning, deep learning, computer vision, natural language processing, and robotic process automation to optimize sports performance, increase fan engagement, and provide sports intelligence. Whether it is optimizing athletes’ performance, assessing their physical condition and injury risks, making data-driven decisions for game planning, or enhancing fan engagement through personalized and interactive experiences, AI is at the forefront of transforming the entire sports industry, from athletes and coaches to sports organizations and fans.
2. Data Collection and Processing in Sports Analytics
The use of tracking sensors is now a common practice in many sports, and their use is growing. Global Navigation Satellite System (GNSS), Radio-Frequency Identification (RFID), and Local Position Measurement (LPM) are the most commonly utilized technologies for tracking athletes. These technologies offer unique features and have both strengths and weaknesses. GNSS is lightweight, unobtrusive, and can be used to track athletes in a wide range of outdoor environments. Its main weaknesses are the relatively low measurement quality and the fact that it cannot be used in indoor environments. In contrast, RFID and LPM offer high measurement quality but have other weaknesses. RFID is low-cost but can only be used for event-based tracking as it usually does not provide a continuous position estimate.
Sports generate vast amounts of complex data through the extensive use of sensors in both training and game environments. Sensor technology contributes to vast and high-frequency data for each team or individual athlete over time. Multimedia data (e.g., images or video) is also increasingly used and adds further complexity to data collection. Such data is typically temporal for both the training and playing environment, for example during matches. Time-stamped data represents one of the most common and foundational types of temporal data in sports and is often used to evaluate performance and make decisions — both during training and in competitions.
2.1. Types of Data Collected in Sports
Furthermore, the type of data that is used in the analysis and presentation of sports is categorized into four main types: (1) qualitative, (2) quantitative, (3) connected, and (4) automated. Qualitative data are generally observed and recorded descriptions or classifications. In the context of sports, examples of qualitative data are commentary, written reports, interview notes, and generally any narrative about sports. Quantitative data refers to the countable or measurable observations that are often represented using numbers. Examples of quantitative data in sports include scores, tables, and statistics on sports performance, fitness, and health. Relatively new types of quantitative data in sports come from the fields of sports technology and sports science, which use specialized equipment to capture data such as speed, time, distance, and physiological indicators. These data are often presented in the form of performance or health indicators. Unlike qualitative and quantitative data that can be in the form of independent isolated observations, connected data in sports is defined as numerous related data observations. An example of connected data is the sports competition calendar which contains interconnected data about sports events. Automated data is the data produced from technology-generated reports, feeds, or databases. It is often presented as a news report or an information dashboard.
There are a plethora of data resources and data types available in the sports domain, from grassroots level to professional sports. Right from competition results to training data, and recruitment data to equipment sensor data. Hassabis, Stuhlmüller, Doya, and Sherlock disclosed an exemplary list of data types in their patent application, which includes training data, physical measures, performance on one or more given tasks, stress measures, anthropometric data, demographic data, dietary data, genotype data, proteomic data, neuroimaging data, and blood biomarkers. Apart from the data associated with the players, teams, and management also deal with and use media-related data like social media data, and event-related data such as ticketing data, event broadcast, or streaming data. In recent years, a special type of data called event stream data has become prominent and is continuously being generated for various events and sports via different channels. Event stream data is the new type of big data associated with the rapidly growing sports intelligence domain. The increasing volume of sports data has been exposed in research and industry, and the potential of sports data has been explored for sports management and marketing, as well as for media, broadcasting, and journalism.
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3. AI Applications in Sports Performance Optimization
Given the explosion in available sports data, it is not surprising that many of the most recent, advanced AI models in sports analytics are designed to predict, forecast, or optimize. Such predictions range from very short-term, real-time outcomes, to forecasts of likely future events on horizons from minutes to games to an entire season. These joints include player and team performance, scoring, and outcomes; explanations of play events and game flow; and anticipations and reactions of the public (i.e., fans and sports bettors). In addition to the popularity of sports, the evaluation of such models is an incredibly challenging task that has fueled interest in the application of AI methods not just within the sports analytics community but among researchers in other domains as well.
Sports performance optimization is one of the most heavily invested areas of sports analytics. With the explosion in the availability of sports data during the last decade, the sports analytics community’s focus shifted from the research of costly specialized data sources to applying cutting-edge AI models to any of the myriad open sports datasets, in search of better player and team performance. That focus remains today, with new research utilizing AI methods to enhance player and team performance across a wide variety of sports including basketball, baseball, American football, soccer, cricket, and more. In this chapter, we review several key AI methods applied to optimizing sports performance. Specifically, we address AI methods related to prediction, forecasting, and real-time decision-making. Since the effectiveness of decision-making models are almost always judged through outcomes (i.e., reliance on prediction models), these two evaluation criteria cannot be completely separated. The primary difference is that decision models generally require more complex model architectures because they must incorporate multi-step structure and consider timing and joint action by other agents (e.g., players or opponents).
3.1. Player Performance Prediction and Injury Prevention
Injury is a major concern in sports at all levels, and occurrences of injury can not only disturb a player’s career but also disrupt a team’s season. Athletes can now be monitored using wearables, which provide continuous streams of multimodal data, and there is potential to use AI to prevent injuries via the analysis of these data. AI can not only model the relationship between the risk of injury and various factors, such as previous injuries, game workload, and player talent but can also help design training strategies that mitigate the risk of injury over time. These data-driven strategies are likely to outperform traditional heuristic strategies and can be further augmented by taking expert knowledge into account.
AI has been employed to predict and optimize player performance along various dimensions. It can predict how well a player will perform under various conditions and offer insights into game and player statistics. It can also relate these performances to external factors and features (e.g., social media data), as well as offer predictions and visualizations in real time. AI can also be used to optimize a player’s physical and mental condition before a game. Recovery and nutritional plans can be offered based on external data, and the plans can be adjusted in real time during a game.
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4. Ethical and Privacy Considerations in AI in Sports
AI is used in different sports for different goals. In cycling, for example, it is used to predict how long it will take the current breakaway to get caught, and the optimal time to launch an attack. In swimming, AI is used to schedule the swimmers’ heats at the start of the competition and to make them break the least number of national records. The use of AI in these cases relates to optimizing performance. In other sports, AI has been used to predict the outcome of games or to better understand the opponents’ tactics. While these developments are interesting and there are many more possible applications, we have to keep in mind that using AI in Sports is not only about improving performance. Many ethical implications around that usage need to be taken into account.
In recent years, new technologies such as the extensive use of cameras, sensors, and advanced algorithms have brought the sports industry to the next level. These new technologies, together with a specialization in Sports Analytics and the use of Artificial Intelligence, have a broad range of applications in sports, varying from predicting outcomes to improving the performance of individual athletes or teams. With these promising new developments in the field of AI in Sports, ethical and privacy considerations are often no longer receiving the needed attention. It is important to realize that the use of AI in Sports impacts not only the athletes but also other stakeholders such as the audience and more in general the society. In this chapter, we will discuss several important ethical and privacy considerations in the field of AI in Sports and Performance Optimization.
4.1. Data Security and Privacy Regulations
Ensuring data security and privacy regulations compliance is a challenge with an increasing volume of data flowing from multiple sources for ingestion that undergo a complex ETL process before different AI models governed by different stakeholders can access the data to provide valuable insights. The rapid advances of domain-specific AI models for talent identification, injury prevention, and performance enhancement raise specific data security requirements. The strict use of access controls through a role-based access mechanism, different from the GDPR recommended pseudonymization, orchestration layer is a way to provide security that can be implemented across different AI model pipelines. Security through AI/ML context-aware policies can be enabled for specific stakeholders on the data or model level. Furthermore, specific AI models can enable existing GDPR compliance mechanisms to be extended, such as permitting new ways of the purpose limitation processed defined by specific sports organizations based on the data subject’s consent.
The fast-evolving landscape of AI in sports also demands the accompanying data source requirements change at a fast pace. Advanced sensors, the Internet of Things, and increased data-capturing techniques facilitate data availability for all stakeholders. However, as a critical domain, data security and privacy regulations require special attention. The General Data Protection Regulation (GDPR) is the most advanced data protection regulation that puts a considerable burden on data controllers and processors on how to collect, process, store, and share personal data. In sports, talent identification, injury prevention, and performance enhancement are at the forefront of using personal data, raising ethical issues where the athlete should have the autonomy and right to decision-making and consent.
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5. Future Trends and Innovations in AI in Sports
The future of AI in sports is going to witness a lot more innovations and increasing use of already existing techniques. Innovations will also bring in a lot more interdisciplinary work, combining expertise from the sports domain, computer science, robotics, and biomechanics. Newer methods will be developed for more specialized fields of play sports, to make the solution more generalized and adaptable. The concept of Explainable AI will find even more importance, as coaches will like to learn how the decisions are being made, and based on what information. Lastly, the growth of AI will not only be limited to the professional sports teams but it will also be used extensively in the amateur and collegiate sports teams.
Today, artificial intelligence (AI) is one of the fastest growing technologies, finding applications in almost every field, and sports is no different; varying from back-office management to on-field training and game performance. AI has helped immensely in defining new strategies, enhancing player performance, preventing injuries, and ultimately improving the chances of a win. As of writing this chapter, the application of AI in sports is still at an evolving stage. In this chapter, we have presented an overview of how AI is being used in sports, the techniques and algorithms used in developing AI-led sports solutions, and the impact of AI in sports.
5.1. Emerging Technologies in Sports Analytics
Artificial Intelligence (AI) is playing a major role in this transformation. Advances in AI are the single most important enabler of the modernization of all sectors, including sports. AI is unlocking a revolution in sports through the creation of entirely new or the enhancement of existing products, services, and experiences. It is connecting physical and virtual sports domains, which in turn can leverage data from other sectors such as entertainment, finance, or healthcare. Today, thanks to AI, players, teams, and sports organizations can optimize their performance using adaptive and personalized tools that were previously unavailable.
Sports fans have more access to player and team insight information than ever before. Advances ranging from player tracking technology to augmented reality are continuing to fuel the evolution of sports analysis. The development of emerging technologies in sports analytics is taking it to a new level by not only improving the fans’ viewing experience but in developing strategies and making decisions that improve player and team performance. These types of technologies are optimizing how sports are organized, managed, played, and viewed across the entire ecosystem.
