Computer Vision in Sports: Applications, Challenges, and Sports Datasets

The market for computer vision in sports is rocketing. It will jump from $2.39 billion to $3.1 billion by the end of 2025. That’s a massive 29.8% yearly growth compared to last year! In fact, by 2029, it’s predicted to hit $8.7 billion. This wave comes from AI going transnational in global sports. Furthermore, smarter analytics and better training fuel this rise. Other drivers include data security, wearables, and tech expansion. Big trends to watch? AI in esports, predictive betting, ethical concerns, sports science alliances, and AI-personalized fan experiences. The future of sports technology? Smarter, faster, and more high-tech than ever! 

But everything comes with challenges, and Computer Vision technology is no exception. This article will dive into the multifarious applications and address the various challenges with solutions. Also, do not forget to take a look at the exemplary sports data sets that have propelled the conquest of many sports associations! 

Overview of Machine Learning and Computer Vision in Sports

Most sports involve quick movements. It’s tough to keep up with the action by just watching. That’s where machine learning sports come in. It helps track those swift motions. The models of Computer vision help process tons of visual data during games. This allows real-time decisions, better training, and much more. These tools are vital for collecting data, analyzing sports, and making predictions.

What is Computer Vision in Sports Analytics?

Sports analytics is all about data. Sports analysis uses computer vision to pull insights from sports videos and images. Basically, it digs into sports data to spot patterns and key moments quickly. It includes smart algorithms to track players, balls, and gear. They follow movements with pinpoint accuracy.

Computer vision in sports training is like a digital coach with super sight. Sports organizations are embracing computer vision to track athletes and fine-tune game strategies. In simplest terms, cameras grab high-quality game footage. Then, smart algorithms dissect every move and play. All this helps sports organizers with

1. Game Planning for Professional Sports
2. Player performance analysis
3. Performance Improvement
4. Injury Prevention
5. Sports Recruitment, etc.

Football, Cricket, Tennis, and Basketball rely on smart sports analytics using computer vision. Machine Learning and smart AI-powered sports computing predict outcomes. Natural Language Processing gauges fan sentiment which further delivers insights for sports journalists. Lastly, Data Analytics integrates insights to help coaches tailor training sessions.

What are the Applications of Computer Vision in Sports Analytics?

Artificial intelligence and deep learning are still evolving. However, the impact is already evident in key industries like Healthcare and security. Let’s see some computer vision application examples in the sports industry:

Player Tracking and Analysis with Computer Vision

For years, Computer vision in sports has been boosting athlete performance through motion tracking. Tracking movement helps coaches pinpoint areas for improvement while athletes can focus their training.

Key highlights of Computer Vision Applications in Sports:

• It tracks athletes during training and competitions.
• It helps detect or follow players’ movements in different sports. 
• Coaches use this to study player movements and behavior.
• By tracking how a player moves, analysts can spot patterns and performance trends.
• It can suggest the best player positions and compare them to actual positions in real time.

A great example is IBM’s cognitive coaching system. It is used by the U.S. Women’s National Soccer Team. It reviews video footage and gives real-time feedback.

Nowadays, computer vision goes even further. It now can track joint and limb movements to provide even more detailed insights. High-resolution cameras capture every move of sports professionals. Computer vision algorithms then analyze the data. With these insights, training becomes sharper. Coaches can focus on weak areas. Ultimately, the whole team gets better.

Apart from tracking players, Machine learning in sports performance also delivers context. For example:

During cricket matches, it can predict if a player has the ball or if they are defending, passing, or running.

In basketball, computer vision and machine learning tech tracks player movement, identifies the best shooting spots and helps with defensive strategies. Coaches love this data-driven approach, which guides them in improving tactics.

Intelligent Athlete Identification and Classification

Computer vision in sports is making athlete identification easier. Using AI, it classifies and tracks athletes in video footage. There are several ways to identify athletes. The popular ones are:

Method 1: Facial recognition software—It looks at features like hairstyle and focuses on face shape. It basically gives athletes a digital fingerprint!

Method 2: Tracking body movement- Advanced AI and computer vision track the athlete’s position during the game. It detects the specific key points on the body (like knees, elbows, wrists). These are marked on each frame of a video. The result creates a “skeleton” representation of the athlete’s movement.

Data annotation services offer the raw data to train machine learning sports models with key point annotation and semantic segmentation for intelligent athlete identification and classification. With this data, computer vision enables progress monitoring over time, and coaches can compare athletic performance across players.

Improving Officiating and Rule Enforcement with Computer Vision

Computer vision in sports is transforming how administrators make calls in real time. Scrutinizing video footage makes hard judgments more painless in a variety of sports. The result? Fewer mistakes, fairer outcomes, and better overall integrity in games.

For example, the smart computer system can confirm if a football crosses the goal line or if a tennis serve is out. This gives officials clear, objective evidence to back up their choices.

Computer vision applications are also stepping up in detecting rule violations in popular sports. One great example is the Video Assistant Referee (VAR) system used by FIFA. It helps officials make the right call on controversial moments.

VAR uses video footage to review key events, like-

1. Whether a goal was scored
2. Whether a foul occurred
3. If a player was offside

AI in sports provides super accurate results—much more reliable than the human eye. No more guesswork in game-changing decisions!

Monitoring Injury Risks with Data Science and Computer Vision

Data science and computer vision sports are spotting injury risks before they strike. Knee injuries, especially ACL tears, are an absolute game-ruiner. In the U.S., over 200,000 athletes confront this setback every year!

Researchers use multi-angle video analysis to examine lower-body injury hazards. They focus on motions leading to these intense injuries.

Key uses:

1. To track player movements during games and practice.
2. In sports like aerobics, computer vision tracks ankle sprain risks.

Computer vision in sports training also helps prevent injuries. By identifying risky movements early, like awkward landings, coaches can take precautionary measures.

Computer Vision in Athlete Rehabilitation

Computer vision is not just for injury detection. Applications of computer vision in sports are also improving rehabilitation for athletes. Researchers are designing applications for this purpose.

One example is tracking shoulder injury recovery. Benefits are-

1. These systems provide accurate monitoring throughout rehab.
2. Doctors and therapists can design better recovery plans.
3. By tracking movements during recovery, computer vision offers key insights.
4. This helps create personalized exercises for athletes.

The result? Faster, more practical recoveries.

Object Detection and Ball Tracking in Sports with Computer Vision

Real-time object detection is key. It tracks sports equipment, like balls. AI-based ball tracking uses machine learning in sports performance. Tennis, cricket, and badminton rely on it. In sports like basketball, volleyball, and soccer, it gets trickier. The ball can be hidden or move fast. But computer vision keeps up!

The algorithm records the ball’s movement in 3D. It can pinpoint where the ball hits the ground and even predict its next move. This improves shooting and passing precision. Ball tracking also helps key areas like:

• Spotting the ball in play
• Tracing its trajectory (Mapping its movement mid-air)
• Predicting game outcomes

But there’s more! Coaches use this data to fine-tune strategies, break down patterns, and make smarter, winning decisions.

AI in Batting and Pitching Performance

Computer vision applications are driving accuracy in bat-and-ball sports. Cricket and baseball are prime examples. These two games share a simple goal—hit the ball. But now, technology is upping the game!

• For batters: Computer vision follows the ball’s path. It predicts where it’ll land and this data helps batters adjust their swing.
• For pitchers: The system analyzes ball movement, and instant feedback further sharpens precision and command.

Intelligent moves all around! All thanks to artificial intelligence and computer vision!

Automatic Refereeing with Artificial Intelligence

Sports analytics using computer vision is refining sports officiating with automatic refereeing.

Take the Hawk-Eye computer vision model, for example. Used in cricket and tennis, it tracks the ball’s path to determine if it’s fair or foul.

This system uses a network of cameras and compares the ball’s trajectory to a virtual model of the playing field. It’s incredibly accurate—within a few millimeters—way better than the human eye.

In the NFL, “Next Gen Stats” tracks every player’s position on the field. This data helps officials make smarter calls, like whether to flag a penalty.

Forecasting Sports Winners with Machine Learning

Forecasting the potential winner of a sporting event with AI is becoming a reality.

How does it work?

The machine learning model analyzes the data collected by computer vision. The algorithms track athletes’ key actions during a match. Often, software developers collaborate with third-party data collection services to compile data. Then, software is trained to predict the winner of a match.

These predictions can influence betting decisions. It gives a statistical edge on who’s more likely to win. For example, to predict football match outcomes, two key systems are used that are based on-

• Deep Neural Networks (DNNs)
• Artificial Neural Networks (ANNs)

In real-life use cases, deep learning models predicted the FIFA World Cup 2018 outcomes. They achieved a 63.3% prediction accuracy. Pretty impressive, right?

Identification of Different Exercises in Fitness and Sports

Computer vision in sports training is becoming an essential tool for tracking fitness progress. These systems use real-time video analysis to

1. Monitor the number of repetitions an individual completes for a specific exercise
2. To provide feedback on the quality of each rep.

In sports medicine and coaching, this feedback helps improve an athlete’s form and overall performance.

Next, we have Image recognition technology. It identifies exercises like squats, lunges, and push-ups, ensuring every rep counts!

High-performance deep learning systems analyze body positions. They monitor joint movements through key points and human-pose estimation. This guarantees precise tracking and steady progress.

Audience Engagement Through Immersive Experiences

Computer vision applications go beyond the field and in the training rooms—it’s also upgrading how fans watch and interact with sports. It powers augmented reality (AR) features. Fans can now experience games from the athletes’ perspective or feel like they’re in the stadium.

Key Highlights

1. Cameras track the action and provide close-ups rather than just wide shots of the court.
2. Fans don’t miss key moments anymore—these cameras know exactly where to focus.
3. Computer vision in sports analyzes fan reactions during games. This gives sports organizations valuable insights to boost engagement and create deeper fan connections.

For instance, the audience now can see a live football match with real-time stats, player tracking, and 3D replays—all thanks to computer vision. These immersive features don’t just level up fan engagement; they also open up new revenue opportunities for sports organizations.

Even Amazon is getting in on the action! Partnering with Formula 1, they’ve brought AI and augmented reality together to enrich race broadcasts. Formula 1 teams gather tons of data from sensors in their cars, which AI processes and visualizes during races.

Machine learning sports models give fans a deeper look at the race, with real-time updates on team and car conditions. From the POV of the wider sports industry, it’s a big business—Formula 1 makes hundreds of millions from these broadcasts. The Machine learning algorithm offers even more growth potential in this space.

Enhancing the Spectacle with Media and Ads

Computer vision in sports it’s not just about stats. Sports journalists are also making the most out of it!

Example- Mirriad places real-time virtual ads on surfaces during broadcasts. This brings in relevant ads for viewers. Furthermore, it generates extra revenue for sports teams.

Another example is Stats Perform, which is an advanced computer vision system that is all about data visualization. They cover over 500,000 competitions a year, processing 7 petabytes of Free Image Datasets for Computer Vision with AI models. Their goal? Making complex data easy for fans to digest.

Enhancing Crowd Monitoring and Safety

Meanwhile, security is getting a tech upgrade with the applications of computer vision in sports. In the field of computer vision, cameras aren’t just focused on the field—they’re watching the stands, too. These applications are seen at major sporting events such as

1. Olympics
2. World Cups
3. Tennis tournaments

Based on computer vision and AI, these systems leverage real-time computer vision to detect and track objects in crowds across a network of cameras. Furthermore, Deep learning models help-

• Identify items like bags, backpacks, and phones in a crowd, letting security act fast.
• Specialized applications go further, recognizing potential threats, such as violent behavior or individuals carrying weapons.
• These systems can also detect unattended objects. This helps ensure safety and security during large-scale events.

The real-life computer vision applications include Ipsotek’s VISense. With Generative AI integration, it uses VLMs to achieve detailed scene understanding and contextual insights. It tracks crowd size and identifies potential security risks. Additionally, facial recognition tech is being used to automatically ticket disruptive fans at soccer games.

All in all, computer vision technologies are improving every aspect of the sports experience!

Challenges in Sports AI systems

Matching With Human Intuitional Expertise

Computer vision in sports offers amazing analytics. But it can’t replicate the intuition of a seasoned coach.

For example, an AI might analyze a soccer player’s data to suggest tactical moves. However, it can’t consider the player’s mindset or adaptability in high-pressure moments.

The accurate data labeling, to some extent, trains Machine learning in sports performance to help AI integrate richer context.

Interpretability and Transparency

AI algorithms, especially deep learning, are often called “black boxes.” That means their decision-making process is a bit of a mystery.

For example, AI may select a player for a basketball team without providing clear reasoning, which can lead to skepticism among coaches.

Accurate data labeling can help improve model transparency by ensuring that training data is well-documented and traceable. When data is properly labeled, computer vision applications in decision-making become more transparent. This makes decision-making easier to explain and builds trust with users.

Bias and Equality

Machine learning in sports is only as good as the data on which they are trained. If the data is biased, it can result in unfair decisions. For instance, if an AI system for scouting talent is trained primarily on data from a specific region or gender, it may overlook qualified athletes from underrepresented areas.

Accurate data labeling helps AI make better, unbiased decisions. The expert annotators ensure that the data is diverse and representative. With well-labeled, balanced data sets, AI can make more inclusive decisions. This way, all athletes get an equal shot. Everyone deserves their moment to shine!

Overfitting of Models

Overfitting occurs when an AI model nails the training data but flops in real situations.

For example, a machine learning sports AI model designed to predict the outcome of a tennis match might only perform well for players with similar playing styles to those in the training set.

Accurate data labeling helps solve this. It ensures the training set is diverse and varied. This allows the model generalize better and improves accuracy across many different scenarios.

Data Privacy

The data gathered through an AI computer and sports analytics on athletes’ performance can sometimes lead to privacy concerns.

Accurate data labeling does more than simplify data collection. It ensures personal data stays secure. By categorizing data carefully, it complies with privacy standards. This protects athlete trust while enabling AI to work smoothly.

Some Sports Data Sets Used by Major Sports Organizations

Soccer Data Sets

The UEFA Champions League Dataset includes top goal scorers, all-time rankings, and more. The FIFA 2022 complete player dataset is packed with data. It spans from FIFA 2015 to 2022. Inside, you’ll find over 19,000 players. Plus, there are more than 100 attributes.

FIFA 18 focuses on players from the 2018 edition. This dataset includes player-specific attributes.

The historical soccer results datasets cover half-time and full-time scores. They also include player stats from European and international leagues.

Next, there’s football.db. It’s an open-source database. You can freely use it in any programming language. The international football results from 1872 to 2018 include over 40,000 match results. This spans from the very first official match to 2018!

Lastly, the World Cup Dataset shows all information about historical World Cups and all match data.

Football Data Set

NFL Play-by-Play Data 2009-2018 contains information on:

• Players
• Results
• Win probabilities
• Game situation
• Miscellaneous advanced metrics

Another example is CFB Stats. This is downloadable in CSV file format. Data includes:

1. Kicking Statistics
2. Passing Statistics
3. Punt-return Statistics
4. Rushing, scoring, and punting data.
5. Kickoff-return Statistics
6. Kickoff Statistics

Basketball Datasets

Here are the basketball datasets:

• Michael Jordan Career Regular Season Statistics
• Shaquille O’Neal Career Regular Season Statistics
• WNBA 2014 player stats
• NBA shot logs (2014-2015 season): Includes player, shot location, defender, distance, and shot clock time.
• NBA Player of the Week Data: 1984-1985 to 2018-2019 seasons.
• NCAA Basketball: Play-by-play and box scores from 2009 and final scores since 1996.

Racing Data Sets

The racing datasets include

1. Ergast Formula One Dataset: an experimental web service offering historical motor racing data for non-commercial use.
2. Formula 1 Race Data: Covers results from the 1950 to 2017 F1 seasons, including information on constructors, drivers, lap times, and pit stops. MotoGP Dataset: Featuring statistics from the official MotoGP.com database.

Baseball and Hockey Data Sets

The baseball datasets include the Historical MLB Scores & Odds Dataset 2010-2020. It covers MLB seasons from 2010 to 2020, with data on-

1. Run-lines
2. Opening and closing money lines
3. Totals (over/under).

There is also Lahman’s Baseball Database, which offers all-inclusive data on Major League Baseball stats from 1871 to 2018, including-

1. Batting and pitching stats
2. Standings
3. Team stats
4. Managerial records
5. Post-season data.

For hockey, the National Hockey League Player Offensive Statistics Data Set provides yearly offensive stats for every NHL player from the 1940 season to 2018.

Conclusion

Computer vision application examples are seen across sports, whether baseball, football or even golf! This article explored the most innovative ways that computer vision in sports is ruling the global sports arena. If you plan to train your sports AI models using computer vision techniques, you will need your own image or video data. And that’s where Annotation Box steps in, helping you create spot-on ground-truth data for your computer vision tasks. Contact us today to empower your vision! Get tried-and-tested datasets for computer vision in sports training from expert annotators.