Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions. It has been applied to various fields and domains, such as medicine, education, finance, and entertainment. But what about sport? How is machine learning changing the way we play, watch, and enjoy sports?
In this blog post, we will explore some of the ways that machine learning is transforming sport, both positively and negatively. We will look at some examples of how machine learning is used to enhance performance, prevent injuries, analyze tactics, improve fan engagement, and create new forms of entertainment. We will also discuss some of the challenges and risks that machine learning poses for sport, such as ethical, legal, and social issues.
Machine learning and performance enhancement
One of the most obvious applications of machine learning in sport is to help athletes and coaches improve their performance. Machine learning can help analyze data from various sources, such as sensors, cameras, wearables, and biometrics, to provide feedback, insights, and recommendations. For example, machine learning can help:
- Optimize training schedules and routines based on individual needs and goals
- Detect and correct errors or weaknesses in technique or strategy
- Monitor and manage fatigue, stress, and recovery
- Identify and exploit strengths and opportunities in opponents or competitors
- Enhance decision making and situational awareness during games or matches
Some examples of machine learning tools that are used for performance enhancement in sport are:
- STRIVR: A virtual reality platform that uses machine learning to create realistic simulations of game scenarios for training purposes. It is used by various teams in the NFL, NBA, MLB, and NCAA.
- Second Spectrum: A computer vision system that uses machine learning to track and analyze the movements of players and the ball in basketball. It provides real-time data and insights to coaches and broadcasters.
- WHOOP: A wearable device that uses machine learning to measure and optimize the physiological state of athletes. It tracks heart rate variability, sleep quality, recovery, and strain.
- Zone7: A platform that uses machine learning to predict and prevent injuries in athletes. It analyzes data from various sources, such as medical records, performance metrics, and environmental factors, to identify patterns and risk factors.
Machine learning and fan engagement
Another application of machine learning in sport is to enhance the experience and engagement of fans. Machine learning can help create more personalized, interactive, and immersive ways of consuming and enjoying sports content. For example, machine learning can help:
- Customize content delivery and recommendations based on user preferences and behavior
- Generate highlights, summaries, statistics, and insights from live or archived events
- Create virtual or augmented reality experiences that simulate being at the stadium or on the field
- Enable social media interactions and gamification features that increase participation and loyalty
- Produce synthetic media or deepfakes that alter or generate realistic images or videos of athletes or celebrities
Some examples of machine learning tools that are used for fan engagement in sport are:
- IBM Watson: A cognitive computing system that uses machine learning to provide various services for sports fans. For example, it can create personalized highlight reels, generate natural language summaries, answer questions, and provide insights.
- WSC Sports: A platform that uses machine learning to automatically create video content from live sports events. It can generate highlights, clips, stories, and compilations based on user preferences or requests.
- LiveLike: A platform that uses machine learning to create virtual reality experiences for sports fans. It can stream live events in 360-degree video, enable social interactions with other fans or commentators, and provide interactive features such as polls or trivia.
- Reface: An app that uses machine learning to create realistic face swaps or deepfakes. It can replace the faces of athletes or celebrities with the user’s own face or someone else’s.
Machine learning and new forms of entertainment
A third application of machine learning in sport is to create new forms of entertainment that combine elements of sport with elements of art or gaming. Machine learning can help generate novel content or experiences that challenge the boundaries of traditional sports genres. For example, machine learning can help:
- Create artistic or aesthetic expressions of sports data or events
- Generate synthetic or fictional sports scenarios or narratives
- Enable interactive or participatory forms of sports entertainment
- Create hybrid or cross-disciplinary forms of sports entertainment
Some examples of machine learning tools that are used for new forms of entertainment in sport are:
- Google Cloud: A cloud computing service that uses machine learning to provide various solutions for sports entertainment. For example, it can create artistic visualizations of sports data,
generate fictional sports stories or scenarios, enable interactive games or simulations based on sports events, and create hybrid forms of sports entertainment that combine elements of music, dance, or theater. - OpenAI: A research organization that uses machine learning to create artificial intelligence systems that can perform various tasks or challenges. For example, it created OpenAI Five, a team of AI agents that can play the game of Dota 2, a popular multiplayer online battle arena game. It also created OpenAI Codex, a system that can generate code or text based on natural language commands or queries.
- NVIDIA: A technology company that uses machine learning to create graphics and video processing solutions for various applications. For example, it created NVIDIA Omniverse, a platform that enables the creation and simulation of realistic 3D environments and characters. It also created NVIDIA StyleGAN, a system that can generate realistic images of faces or objects based on style or attributes.
Machine learning and challenges for sport
While machine learning offers many benefits and opportunities for sport, it also poses some challenges and risks that need to be addressed. Machine learning can have negative impacts on the integrity, fairness, and ethics of sport, as well as on the privacy, security, and well-being of athletes and fans. For example, machine learning can:
- Enable cheating or manipulation of results or performance by using unauthorized or unfair methods or tools
- Create biases or discrimination in data collection, analysis, or decision making based on factors such as race, gender, or age
- Violate the rights or consent of athletes or fans by collecting, using, or sharing their personal or sensitive data without their knowledge or permission
- Expose athletes or fans to cyberattacks or malicious actors that can compromise their data, devices, or accounts
- Harm the reputation or image of athletes or fans by creating false or misleading information or media
Some examples of machine learning challenges or risks for sport are:
- The use of performance-enhancing drugs (PEDs) that are designed or optimized by machine learning algorithms to evade detection or regulation
- The use of facial recognition technology that can identify or track athletes or fans without their consent or awareness
- The use of deepfake technology that can create fake or manipulated videos of athletes or celebrities that can damage their credibility or reputation
- The use of hacking or phishing techniques that can access or steal the data, devices, or accounts of athletes or fans
- The use of trolling or harassment techniques that can target or abuse athletes or fans online