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How is Artificial Intelligence used in the Sports industry? Applications & Use cases

09 Sep 2022
Artificial Intelligence in Sports

Artificial intelligence breaks into all possible areas of our lives. It helps us to automate processes, make decisions and perform tasks instead of a human. In some situations, this technology has become a part of us, and we can no longer imagine our life without it.

Surely, many of you have used diet apps, the so-called AI diet consultant. This is also the work of Artificial Intelligence algorithms suggest diets based on your characteristics. Or during COVID-19, we often used apps for doing sports at home, here AI algorithms evaluate your productivity and the correctness of the exercises performance.

If you are not familiar with these technologies, read our articles to deep dive into:

Artificial Intelligence in sports is of great benefit, this is a new opportunity to make decisions in just a couple of seconds, without waiting for the end of the match and get accurate analytics.

After such an intervention, its effectiveness has become interesting for various sports, so now the market is only expanding.

In 2020, the global AI in sports market was estimated at $1.4 billion and by 2030 it is predicted to grow by 30%, which is estimated at $19.2 billion. These numbers really prove it!

In this article, we will go through different cases of using AI in sports, and learn how data is collected for game analysis.

Table of content:

  • AI applications in sports
  • AI in different sports
  • Types of sports data
  • Sources of sports data
  • AI sports real-world examples
Artificial Intelligence in Sports Industry

AI applications in sports

Individual Trainings

As we mentioned earlier, the coronavirus outbreak has cut us off from our favorite gyms, so new solutions have had to be found. First there was a transformation into a workout video, but it didn't suit anyone.

Big data has taken over sports so fast that you can now spend hours sorting through the best mobile sports apps. The main features are data collection about exercises and diets. Everyone benefits - coaches have the opportunity to make plans for students, monitor their performance and manage it all in one place. Students feel their progress in numbers not only on the scale, but also in analytics, which presents characteristics such as muscle mass, fat percentage, calories burned. Computer Vision in sports can completely replace a coach, because by turning on the camera on your phone you can check the correctness of the exercise, count approaches and get advice.

Below you can see our yoga use case. It works with a pose estimation algorithm that detects human joints.

Training is a key factor in a player's success. It is significant for sports coaches to know in which areas an athlete needs to improve or where they have gaps in order to focus on them during training.

The software is easier to use, but the development of sports wearables and IoT equipment has also come. Special solutions, such as sporting electromyography to capture muscle behaviour, smart shoes, smart video cameras are already being actively developed.

Nutrition (Diet)

Machine learning is ideal for creating personalised nutrition plans for athletes. Using athlete data such as weight, height, schedule, sleep, fat percentage, water balance, he is able to come up with the perfect formula for everyone.

Game Analytics

Just a few cameras on the football field and real miracles happen - you get a smart adviser, journalist and analyst in one bottle.
Computer vision in sports plays an important role, the face recognition algorithm makes it possible to learn all the cunning tricks and tactics of an opponent. The camera cannot mathematically make a mistake, because it accurately reads the trajectories of the ball at an angle. Such events as The hand of God in soccer will not occur again, and the number of dishonest or inattentive goals has already decreased significantly.
In real time, you will receive accurate data on how the match is going - who scored the goal, who gave a pass, who received a penalty. At the end, detailed analytics, not just 2-0, but even with comments and time codes of certain events.

Below you can see our Soccer Game Video Production Solution.

Soccer Game Video Analytics

Player Performance

Do you know how much the most expensive footballer of 2022 is worth? 206 million euros.
Talent acquisition in sports is an expensive thing, big risks and investments. Technology has quickly entered this part of the industry as it automates the process of evaluating a player's potential. With player statistics such as running speed, weight he can lift, goal accuracy, etc. , clubs can evaluate a player's potential and performance.

According to AI in sports research, the results of players and teams improved by an average of 17% and 28% respectively. The analysis was carried out for all sports.

Player Performance Artificial Intelligence
Source: Stats Perform

Streaming and Journalism

Impact of AI caught fire right in the fans heart, because now they have received a bot that broadcasts the match and generates subtitles in all languages of the world. By installing smart cameras with Computer Vision technology, AI is able to detect the key moments of the game and zoom in there. Content creation is made as easy as possible, no more waiting hours after a match to see all the star shots or goals in slow motion.
And of course, analytics, several days to process a 90-minute match already seems ridiculous, because you can get a report from ai technology that captures all the configured events.


People have a habit of congregating at great speed before any event, sports events is not an exception. In fact, no process could automate this process until AI stepped in here. The solution is simple - use a facial recognition algorithm, so fans can enter the stadium without checking tickets. After that, queues are not collected at all, and by collecting data about fans, you can get to know your visitor in depth, their number, gender and age. Forecasts and cognitive analytics are used to predict the likely attendance of the stadium, which helps the timely preparation of goods and products.

Face Recognition Computer Vision Sports
Source: Abc News

Sports predictions

Excitement in sports betting brings a lot of money to the sports industry, which is why technology has not bypassed this part of the business. The big advantage of AI is that it can process a large amount of data, which helps to make predictions more clearly than a person. The main factors for analysis are the composition of the team, the number of goals and passes, the overall strategy based on the analysis of previous matches.

Health and injures

Teams are worried about the health of their athlete, because if anything happens, he can leave the game. Technology is especially applicable here, for example, special wearable technologies are used to track the movements of players and their physical performance during training. To maintain their health, players often undergo physical tests that detect even early signs of illness or injury.
This approach to medicine allows you to maintain the physical shape of athletes and prevent injuries.

How AI Is being used in different sports


The most popular sport in the world has been using AI for several years in the form of training, broadcasts and translation of subtitles. Also, an important part of AI's work is the Goal Line Technology and Video Assistant Referee, helping the referee make fair and accurate decisions.


Here is essential in the velocity and technique of the game. Mostly AI in baseball is used to evaluate the efficiency of players. The throw is evaluated by factors such as reaction speed, force and angle of impact, accuracy.

Baseball Artificial Intelligence


AI in cricket is used to predict and improve game strategy. By using special sensors that are superimposed on the bat, you can hone your skills and identify the best practices.


Fans are often tempted to watch the game from up close, but unfortunately stadiums don't provide that opportunity. IBM Watson is great solution for solve this problem, they taught AI to recognize the peak moments of the match.


There is a popular app called Homecourt that uses AI and Machine Learning. By practicing with this application, players learn how to perform their shots better. The application concludes whether the shot was successful by analyzing the height, the three-point line and the basket.

Artificial intelligence in Backetball Sports
Source: Techcrunch

Sports data types


These high-level statistics can allow you to sum up the entire match with events that happened in just a few seconds. Basic statistics give us typical data about the game - how many goals were scored and by whom.

With the help of these statistics, you will get detailed information on teams, who held the match more often, how many passes were served, etc. You can see an example of such an analysis below.


Soccer Analysis AI in Sports
Source: Sky Sports

Event data

A detailed analysis of an event is a text description or a visual recreation of an event, such as a goal. There is a kind of reconstruction of the field, with the help of which you can see how the event took place from different sides and at what angle (cameras are limited in this case). The advantage is that the analysis is extremely fast and accurate.


AI in Sports Soccer Data Analysis
Source: Sky Sports

Tracking data

As we mentioned above, the disadvantage of the camera is that it is difficult to analyze the game from one part of the field. This method allows analysts to see the field in the form of a projection, where the "dots" that represent people move.

Tracking Data Type Artificial Intelligence
Source: Patrick Lucey at Stats Perform

Sources of data

Video Footage

It is enough to download the video of the match to get the analysis of the game in the form of data that we mentioned above. In this case, computer vision technology is used, which has various algorithms for detecting objects, patterns, postures and movements.

Radio Frequency Identification (RFID)

Special radio frequency devices that help track the location of players on the field are usually built into the players' shoulder pads.


Many sports, such as golf, require ball tracking with visualization of area, radars do it very well.

Wearable GPS Devices

Sometimes on the field you need to know not only about the whereabouts of a player, but also about his state of health - pulse, level of physical activity, and here's is where GPS used for.

AI in Sports real-world examples

NBA court powered by Microsoft Azure
Software that uses AI and Machine Learning to transform analytics. The NBA processes a huge amount of data that is simply beyond the power of a human. That is why they have developed a special platform based on Microsoft Azure.
The main metrics ball screens, passing, defensive scores, shooting.

Gameface AI
This application allows you to get match insights in any sport through video analysis. The main events are a goal, passing the ball, shooting on goal, passing schemes.

A platform has many use cases - from sports to physical therapy. Computer Vision models are trained to detect 44 points on the body, even the most complex movements can be analyzed with ease.

How to Create a Fitness App?

You may think that developing a fitness app is a complicated process, but in fact, adding the latest technology to your application entire investment will redeem. You will be pleasantly surprised by the high interest and engagement of users, because an application with ai technology is not just boring statistics, but real interaction.

If you want to develop a fitness application, we recommend you read our previous article.


The rapid development of AI in sports is due to its incredible value in this industry. Accuracy and big data - this is where machine learning and deep learning are strong. Computer Vision can quickly and precise detect events, objects and movements, which is necessary for sports, because sports are always about the visual world.

Certainly, we have not yet reached the peak of the use of AI in sports, because everything is just beginning. That is why now is the best time to start implementing innovative technology in your products.

We at ByteAnt have over 3 years of experience in developing Computer Vision models. One of our cases in sports is the development of computer vision real-time football analytics, you can take a look here.

Our company is focused on creating smart and innovative ideas for business problem-solving, business automation and better customer service.

Contact us to expand your business opportunities with AI technology.

Want to create Computer Vision model?

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