Computer vision also records the engagement and reaction of the audience in a stadium

Fast and accurate, that is what most sports are about. Although when computer vision has been around for many years, not many people in sports seem to be aware of its values, feasibility and applications to the real stadium and the world.

Computer Vision (CV) is a subfield of artificial intelligence and machine learning that develops techniques to train computers to interpret and understand the contents inside images. Computer Vision aims to replicate parts of the complexities in the human visual system and visual perception by applying deep learning models to accurately detect and classify objects from the dynamic and varying physical world. Many types of sports are often multidimensional systems that incorporate a plethora of data points that make one team or athlete better than the other. Computer vision is a part of the equation.

Computer vision also plays a vital role in the sidelines of the sports industry. It can track the engagement and reaction of the audience in a stadium, teams and leagues which make companies optimize the data. The collected information can be used for customer satisfaction. It also increases revenue and audience. On the other hand, entertainment media and marketing media is created by hands or animations from media specialists with large cost and time. Image solutions reduce the cost spent on sports lives.

Here are some of the Game-changing use cases of computer vision in sports

Validating the performance using In-depth Analysis

Sportlogiq is one of the first companies to dig deeper into AI-enabled analytics in sports. The Montreal headquartered company was founded by former Olympic skater Craig Buntin and other Ph.D graduates in computer vision and machine learning. Sportlogiq works on the in-depth analysis, enabling teams to have the edge in strategy, scouting and performance benchmarking.

Vague scores and shots are not enough for players and coaches to understand the root cause of success and failure in sports. Henceforth, AI and computer vision can understand the basics of a game outcome and give specific conditions or moves to improve the player's statics. Sportlogiq aids hockey, soccer and football teams to make smarter decisions with deeper insights.

The company enables the feature of computer vision through cameras that observe the players' movements, ball trajectories, shots and passes. Then the data is covered into a report which the sports players, trainers and even companies use for improvement and betting strategies. This technique is unveiled by Sportlogiq to more than 60 teams in professional hockey, football and soccer.

Detecting Malfunctions to Ensure Race Safety 

Ford in collaboration with Argo AI has created a deep learning neural network that helps increase NASCAR safety measures. Competitors take the car races knowing that it might cause an accident in the worst case. The 200 mph speed is absolutely vulnerable for an accident. But the worst case is when accidents are caused by sudden malfunctions in racing cars.

Ford jumped into the AI technology when it was trying to make self-driving cars. Ultimately, the team noticed that its algorithm was able to effectively identify the specific models of cars, especially when it came to blurred images. Given that NASCAR pushes the limit in terms of maximum speeds, this capability of the algorithm has proven to be especially relevant. Henceforth, the collaboration of Ford can now detect car malfunctioning and prevent accidents on the track.

Making Smarter Decisions on Players

Last year, the Orlando Magic used sports analytics with the help of technology company STATS to find the next big NBA start. The success in sports is slowly turning towards correctly utilizing the lavish amount of data present in the analytics department. The AutoStats software created by STATS used the data from video footages to find the next star. AI and computer vision are the major technologies that helped AutoStats find a solution.

Earlier, analyzing a player's performance using video footages was only possible with the in-venue cameras installed in the premises of official NBA courts. But today, AutoStats can source players' data from any recorded game, which significantly expands the analytics capabilities and makes the scouting process less complex and more reliable.

The global player tracking market is expected to grow from US$2.1 billion in 2018 to US$7.3 billion by 2023, with an anticipated CAGR of 27.8% during the forecast period.

Improving Shooting Performance through Accuracy

Alan Marty, founder of the Noah Shooting System's has acknowledged that players might need an AI-assisted boost to improve their performance. Therefore, the company uses small computer vision-enabled cameras and sensors that are installed in the rafters above the baskets to track the movement. Dozens of shooting analytics including players' locations on the court, ball rotation speed, shot's arc and shot depth are observed closely.

The system is constantly improving with over 100 shots recorded across many courts. The system allows coaches and players to understand shooting on a granular level. The System now not only records ball trajectory data but also analyzes the players' shooting biomechanics.

Upgrading Audience Viewing Experience

Second Spectrum, an Official Tracking Provider for the NBA, uses computer vision, AR, and players' historic data to superimpose graphics and statistics, like the probability for a player to make a successful shot. Second Spectrum's Court Vision makes basketball broadcasts much more engaging for fans and provides comprehensive analytics for teams on the court.