AI in Sports: Applications and Real-World Examples

Classic wearable devices

Wearable devices like smartwatches and fitness bracelets have become integral tools in tracking many of the metrics outlined above. While wearable technology has been around for some time now—the first Fitbit tracker was released 15 years ago—measurement precision is improving, and the application fields are expanding. 

Wearable devices with GPS sensors track an athlete’s location, speed, distance traveled, and movement patterns. Combined with accelerometers measuring the rate of change of velocity in multiple directions, they offer insights into positioning, speed, movement intensity, and jump dynamics.

Heart rate monitors in wearables track heart rate in real-time by detecting blood flow using optical sensors, which is essential for monitoring cardiovascular fitness, training intensity, and recovery. Electrodermal activity (EDA) sensors measure skin conductivity, which is a good indicator of stress levels and emotional responses during training and competition. 

Blood oxygen saturation (SpO2) monitors are another integral component of modern fitness tracking devices. They use optical sensors to measure the oxygen saturation level in the blood, which is crucial for assessing aerobic capacity and detecting early signs of fatigue or altitude sickness. Finally, electromyography (EMG) sensors measure muscle activation and electrical activity, providing data useful for analyzing muscle fatigue, strength, and coordination.

Extended wearable devices

Since the 2010s, Inertial Measurement Units (IMUs) like GPS tracking vests have become more prevalent in professional sports. These vests, often worn as tank tops with sensors on the upper back, provide unobtrusive and effective GPS tracking.

High-end trackers like Catapult Vector include heart rate sensors, 3-axis accelerometers, gyroscopes, magnetometers, and satellite antennas. These devices track athletes’ positions, distances, speeds, accelerations, jumps, impacts, and heart rates during training and games.

In sports like soccer and rugby, GPS vests are used to analyze player movements and tactical positioning, offering insights into strengths and areas for improvement. This data aids in strategic planning, player substitutions, and training regimens.

Additionally, soccer-specific sensors designed for the calf or feet track physical and technical metrics such as shots, passes, touches, dribbling, and kick speed. Notable products include Footbar, Zepp, Oliver, Next11 (calf sensors), and Jogo, Xampion, Playermaker (feet sensors). The Next11 Smart Ball, equipped with sensors and Bluetooth, reports its position and the nearest player.

Figure 1: Exemplary STATSports dashboard on player movements (Source: STATSports)

AI-powered Cameras

Have you ever wondered where the heatmaps showing zone activity on the pitch come from? Chances are that their origin lies in data that was captured using AI-powered camera tools instead of wearables. Sophisticated computer vision systems like TRACAB, which is used in soccer leagues like the English Premier League and the Spanish La Liga, and Hawk-Eye, which is utilized in a variety of sports, are able to track and analyze player and ball movements with incredible precision. 

Originally developed for tennis to provide accurate line-calling in real-time, Hawk-Eye has since been adopted across various sports including cricket, soccer, and baseball. In cricket, Hawk-Eye is used to track the ball's path, assisting in decisions regarding LBW (Leg Before Wicket) and helping broadcasters provide detailed analysis of bowling techniques and strategies. This technology also contributes to player training by providing visual data on ball trajectories and player movements, enabling athletes to adjust their techniques based on precise feedback. 

The ability of these systems to provide high-resolution tracking and analysis makes them essential for performance improvement and decision-making across multiple sports. Let’s explore the possibilities this recorded data offers to various sports professionals.

AI in Athlete Performance and Health

AI algorithms can analyze player-specific data to identify potential injury risks by examining patterns and anomalies that might indicate stress or overuse. Sudden spikes in player workload, detected by comparing data points like total distance covered, number of sprints, and changes in speed against historical data, can signal a higher risk of muscle strains or joint injuries. This way, the algorithms can support the coaching and medical staff in managing each player’s workload, training, and recovery routines.

Using the data captured with wearable devices, machine learning algorithms can predict player fatigue, injury risk, and future performance by integrating various physiological and performance metrics over time. Here’s how AI can assist in the prediction of fatigue levels, injury risk, and future performance:

Optimizing recovery

Using data from wearables, trackers, and other sources, AI algorithms can support athletes by personalizing recovery plans. One good example of how AI is used to optimize recovery is connected to the single most important activity when it comes to recreation: sleep.

Wearable devices and fitness trackers use accelerometers to detect movement and optical sensors to measure heart rate and blood oxygen levels. Sometimes, they can even detect breathing patterns and snoring with microphones.

From this data, various sleep metrics can be inferred, including total sleep duration, sleep stages (light, deep, and REM sleep), sleep efficiency, and disturbances throughout the night. By analyzing these metrics, AI algorithms can provide a comprehensive picture of an athlete's sleep quality and identify areas for improvement.

AI then combines sleep data with other physiological and performance metrics to create personalized recovery plans. For instance, if an athlete's data shows poor sleep quality or insufficient REM sleep, the AI might recommend specific interventions such as adjusting sleep schedules, using sleep aids, or practicing relaxation techniques before bed.

Additionally, the AI integrates data on muscle soreness, inflammation markers, and overall workload from training sessions to suggest complementary recovery strategies like ice baths, massages, and active recovery exercises. 

AI ensures that recovery plans are specific to each athlete's needs. If an athlete had a particularly intense training session, the AI might suggest an extended sleep duration, followed by an ice bath and a massage the next day to reduce muscle inflammation and promote faster recovery. Recovery optimization is becoming more important as in many popular sports, the number of matches per year and, thus, the workload are rising.

Performance enhancement

Compared to injury prevention and recovery optimization, which use rather general measures, performance enhancement depends on the type of sports. AI can tailor training programs to individual athletes by analyzing sports-specific metrics and personal performance data, optimizing training focuses, exercise selection, and workload protocols. 

In soccer, for example, AI systems might track metrics such as pass completion rate, goals versus expected goals (xG), and the percentage of duels won. By evaluating these metrics, the AI can identify areas where a player excels or needs improvement. For instance, if a midfielder has a high pass completion rate but a low percentage of duels won, the AI might recommend both investigating which kind of duels are challenging for the player and then incorporating more strength and agility drills into their training regimen to improve their tackling and ball-winning abilities.

For a basketball player, the AI might analyze throwing accuracy, defensive metrics, and physical exertion levels. If the data indicates that the player struggles with fatigue during games, the AI could suggest a combination of endurance training and skill-specific drills while also emphasizing the importance of rest days and proper sleep.

Similarly, for a runner, the AI might track pace, stride length, and heart rate variability to tailor a training plan that balances speed work, long-distance runs, and recovery periods to prevent overtraining and optimize performance.

AI in Game Strategy and Coaching

While optimizing individual player performance is crucial, AI also plays a significant role in strategizing and coaching at the team level, which we’ll investigate next.

Opponent analysis and match planning

For outlining a match plan, using all data available for both the own team and the opponent is crucial. Interesting data points on the level of both teams involved include the following:

• Tactics
• Formations
• Pressing level/zones/intensity
• Coverage (man- or space-focused)
• Attack focus areas
• Lineup
• Starting players
• Player positioning
• Mentality
• Performance against favorites vs. against underdogs
• Performance at home vs. away
• Performance in KO vs. league/group stage matches
• Reaction to key events
• Metrics
• Offensive: ball possession, pass rate, shots, efficiency, packing, rebounds, offsides
• Defensive: % of duels won, interceptions, goalkeeper saves, invulnerability, opponent shots

Data transformation and feature engineering are necessary to get insights from chaotic raw facts. For example, transforming formation data into specific features like the number of defenders, midfielders, wingers, and strikers helps simplify complex information into actionable insights.

Tree-based models, such as decision trees and random forests, excel in finding relationships between these features. They can identify patterns in how different formations perform against various opponent strategies, helping predict which formation might be most effective in a match. These models analyze past data to uncover the intricate relationships that drive performance, making them invaluable for strategizing.

The procedure for creating an optimal match strategy involves several steps. First, it’s important to predict the opponent’s strategy based on historical data and current trends. This step includes examining the risks and opportunities presented by the opponent’s likely tactics. Next, AI models predict the best overall strategy for the own team, considering both the predicted opponent strategy and the team's unique characteristics, including player availability and historical success with various formations.

What sounds straightforward in theory is challenging to implement in practice. Accurate predictions require not only current data but also the ability to forecast future conditions, such as player fitness and potential lineups. This involves predicting formations from past formations, assessing success with each formation, and determining which players will be available and in which positions.

AI's ability to synthesize these complex variables into coherent strategies provides a competitive edge, but it requires meticulous data preparation and sophisticated modeling techniques. If you’re interested in what a predictive process could look like, feel free to check out my article on how I predicted the EURO 2024 final.

Video analysis supports strategic planning by allowing AI to steer the selection of key scenes from opponent game footage. This technology analyzes footage to find open spaces, identify patterns in positioning, and understand player tendencies. By breaking down these insights, coaches can bring strategy to the level of individual situations, such as set pieces in soccer and ice hockey.

For example, AI can reveal how an opponent typically defends corners or free kicks, allowing the team to develop specific tactics to exploit these weaknesses. This targeted analysis ensures that strategies are not only broad but also fine-tuned to capitalize on precise moments during the game, providing a significant competitive advantage.

Real-time decision making

AI is not limited to pre-match analysis—providing real-time insights during matches is also possible, transforming how coaches and players make decisions on substitutions, tactics, and play calls.

AI tools like dashboards and decision support systems offer a comprehensive view of the game's dynamics by using real-time data on player performance, opponent behavior, and metrics from IMUs. These systems can highlight key scenes and critical moments, enabling coaches to adjust strategies on the fly. For instance, if an AI system detects that a player is showing signs of fatigue or underperforming compared to their usual metrics, it can suggest timely substitutions to maintain optimal team performance.

In addition to in-game analytics, AI-powered Virtual Reality (VR) simulations offer a revolutionary way for athletes to practice and refine their skills in realistic game scenarios. Applications like BeYourBest, Rezzil, or REPS use VR to put athletes into high-pressure situations, such as taking a penalty in soccer or executing a power play in ice hockey, allowing players to build confidence and improve their decision-making under stress.

They are used by several top-tier sports clubs worldwide for both skill development and tactical training, providing a safe and controlled environment where athletes can experiment with different strategies and techniques without the risk of injury. The immersive nature of VR helps athletes visualize and practice game-specific scenarios repeatedly, leading to better preparedness and enhanced performance during actual competitions.

Conclusion

In conclusion, AI is revolutionizing the sports industry by optimizing player performance, preventing injuries, and enhancing game strategies. Through advanced wearable devices, AI-powered cameras, and sophisticated data analytics, coaches and athletes can make data-driven decisions that improve training, match planning, and recovery processes.

As technology continues to evolve, the integration of AI in sports will only grow, providing deeper insights and greater opportunities for achieving peak performance and strategic excellence.

If you’re interested in kickstarting a career in the growing field of sports analytics, make sure to check out this blog post about how different sports use data analytics.