Football is a game with a long and rich tradition. Many rules and aspects of this game have stayed the same for decades, but that doesn’t mean that there is no room for innovation. AI in football (and AI in sports in general) is the next big thing when it comes to sports performance analytics.

AI sports analytics is a new way to get real-time data on what’s happening on the football field. It combines several technologies, such as machine learning, biometric data, and movement tracking.

So, if you want to learn more about AI in football games, you’re in the right place. This article will explain how everything works and what it could mean for sports analytics. 

How Football Data Helps Analyze Player Performance 

Athlete performance monitoring is not a new thing. In one shape or another, it has been used in football player tracking for a long time now. Performance data helps with injury prevention, workload management, and result prediction.

Many football fans go to the 180Score website to get comprehensive and real-time insights. This way, they can notice details that would otherwise get ignored. As a result, fans can make better predictions on what might happen during those 90 minutes.

Club coaches and managers can also use that data to develop and improve the playing strategy. If, for example, there is a trend of players making mistakes in the last 10 minutes, it is a warning sign that they need a different strategy.

Obviously, there is an insane amount of data being used to analyze player performance.

Machine learning technology has allowed us to process and analyze data volumes that were previously unimaginable. This means that sports analytics are more detailed and precise than ever before. 

Wearables and Sensors for Real-Time Player Data

To be able to analyze data, AI technology needs as much raw data as possible. That’s where IoT (Internet of Things) comes into play. Instead of wearing regular football gear, players wear gear equipped with smart trackers that send information to one another and to the cloud. Sometimes, these trackers are installed in the field.

The trackers usually come in one of these forms:

• Optical-based camera systems
• Local positioning systems (LPS)
• GPS/GNSS systems
• Inertial Measurement Units (IMUs)
• Biometric sensors

The information collected by the trackers is about the player’s movements, speed, heartbeat, how fast they kick the ball, and many other factors. The AI model takes all these pieces of information and processes them, finding patterns and connections that would be missed by the human brain.

Then, the AI model gives valuable insights and answers to the coaches, managers, and other people invested in the club’s performance.

How AI Tracks Fitness, Workload, and Recovery

Here is a brief overview of how AI tracks players’ fitness, workload, and recovery:

AI Tracking Fitness

In the past, players would go through old-school fitness tests, usually once a month. AI models make this kind of fitness tracking redundant. They take historical fitness data for each player and use it as their baseline.

Then, AI receives new fitness data for each player via biometric sensors and compares it to their baseline. If the fitness markers are better, it means the player’s fitness levels are better than before, and vice versa. 

AI Tracking Workload

Workload is basically the amount of physical stress placed on a player’s body while he’s playing the game. Too little workload means that the player is probably not giving his all to the game, but too much workload can lead to quick burnout.

Apart from the effort on the player’s body, this also tracks the effect of that effort. That includes high-intensity distance, heart rate exertion, metabolic power, the number of explosive decelerations (sudden stops), etc. 

AI Tracking Recovery

Recovery is extremely important for a player’s performance. During the recovery periods, players rest and heal minor injuries, which prevents major injuries from happening. AI tracks the player’s recovery by looking at heart rate metrics and sleep cycle data.

By doing this, AI measures if the player’s body has rested and recovered enough to be put under an intense workload again. In other words, it shows whether the player is ready to play again, or he needs more time to rest and recover.

The Future of AI-Based Player Performance Monitoring

AI is becoming more advanced each day, so it is safe to assume that AI-based performance monitoring will also evolve. However, it’s not so easy to predict the future of any of these technologies, but we can make some assumptions based on recent developments.

For example, we might move away from the GPS vests and switch to smart cameras. These cameras could be able to track different points on each player’s body (knees, elbows, feet, head, hands, ankles, shoulders, etc.). This way, the cameras will be able to track players’ movements in real time with high precision.

Another interesting feature that we could see very soon is making precise game simulations based on players’ fitness levels. AI could create digital twins of each player and then use them to run thousands of game simulations. By doing this, AI could make precise predictions about how players might perform in upcoming matches. 

Conclusion

AI sports analytics is giving us a deeper insight into what happens during the game. It has proven to be particularly useful for football matches, where so much raw data is being generated each minute. If you are one of those football fans who like to track performance data on 180Score and similar platforms, keep an eye on future AI developments, as they will change the way you watch football!