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The professional sports industry is committed to finding new approaches to improve player performance while reducing the number of injuries sustained during the season. Player injuries remain a significant challenge, especially as more information is learned about chronic traumatic encephalopathy, more commonly known as CTE. CTE is a progressive brain disease caused by repeated concussions and head trauma sustained by athletes. It is commonly associated with playing certain contact sports, such as football. More cases of CTE have been identified in recent years, putting pressure on football leagues and respective organizations to find a solution that will ensure safer gameplay and mitigate an athlete’s risk of falling victim to this degenerative disease.
The professional sports industry is also struggling with an officiating crisis, which began with the onset of the Covid-19 pandemic. This crisis refers to the shortage of working officials to enforce game rules and make judgment calls during games. In addition to this shortage, the industry has acknowledged the need to improve the accuracy of officiating and reduce the number of bad calls made by officials on the field.
As the sports industry contends with these challenges, an emerging trend in sports automation offers an effective solution. Artificial intelligence (AI) is helping athletes train more effectively, monitor progress, and gain critical insights that aid decision-making and strategy.
AI is also being used to develop advanced safety measures that will result in fewer injuries. Furthermore, AI addresses the industry-wide officiating challenges, as it is being implemented throughout the industry, calling fouls in basketball and balls and strikes in baseball. Data derived from these applications is used to develop game-winning strategies.
Personalized training processes result in fewer injuries, faster recovery times, and improved overall performance. AI is used to produce tailored training and diet programs for athletes based on their individual needs and goals. AI monitors athletes’ progress and interprets key data to alert them of any necessary adjustments. AI is also being employed in all areas of healthcare, allowing teams to utilize wearable devices that track player movements, vital signs, and other health markers to help monitor each player’s overall health.
AI analyzes game footage to identify particular patterns to consider when developing offensive and defensive strategies. Having access to this information provides teams with a competitive advantage. AI uses machine learning algorithms to recognize and track players and objects on the playing field, providing insight into player movement and the ability to forecast potential injuries.
AI can help coaches assess individual performance of athletes and identify key strengths and areas that need improvement. It can illustrate the opposing team’s strengths and weaknesses and assist in competitive strategy development to maximize the chances of winning. It can also support scouting and recruitment.
Many sports leagues, including the NFL, NHL, NBA, MLB, and PGA, currently employ AI in officiating as it ensures accuracy and helps enforce rules. AI is being utilized to verify close calls, identify penalties, evaluate if plays are out of bounds, replay controversial plays, and more.
Additionally, AI is helping shape safety protocols by applying machine learning, computer vision technologies, and data sets from several sources that comprise information on player position, type of play, equipment choice, playing surface, environmental factors, and player injuries to improve treatment methods and injury rehabilitation, as well as predict and prevent injuries from occurring.
The NFL is working in partnership with Amazon Web Services (AWS) to develop computer vision models that will automatically detect the occurrence of concussions and identify the types of forces that cause them. The NFL plans to share this data with equipment manufacturers to spur advancements in equipment design that will provide enhanced protection and benefit players on the field.
Lastly, AI can enhance the fan experience by optimizing camera angles for the best viewing experience when streaming games. Fans can also access AI-generated player insights, stats, and deep match analysis to better understand game strategy and outcomes. It also has yielded benefits in sports betting, providing those that participate with improved forecasting capabilities.
AI assists with pre-game preparation, including training and coaching with nutrition, biomechanics (skill techniques), strategic and tactical offense and defense planning, and team selection. There are several key features of AI in sports automation.
Predictive analytics focuses on predicting and understanding what could happen in the future using machine learning algorithms to analyze past patterns and trends in historical data. It also analyzes player stats, injuries, and prior disciplinary actions to help forecast player performance and identify who is at risk of injury, who is projected to perform poorly, and who is expected to excel. In addition, AI can help teams maximize their strengths and work to correct any weaknesses with valuable insights derived from the data collected.
Video assist automation and data from other computer vision operating systems, specifically object detection, ensure that penalties and goals are called accurately. This technology is a backup to split-second judgment calls by officials, decreasing the margin of error.
Object detection is a segment of computer vision automation that refers to isolating objects in a video or image and correctly classifying them. Object detection is used in intelligent video analytics (IVA) where CCTV cameras are present. This technology helps minimize mistakes and prevents erroneous game outcomes.
On-field tracking systems, such as the Hawk-Eye computer vision system, are used by the MLB in all of its ballparks and training facilities. The Hawk-Eye system software collects data using optical tracking sensors, a cloud infrastructure, and high resolution, high frame rate cameras around the park that provide full-field optical pitch, hit, and player tracking, with some cameras capturing movement at 50 frames per second tracking players and balls, and some cameras capturing movement at 100 frames per second for pitch tracking. This technology has transformed the officiating landscape, yielding marked improvements in accuracy.
Data from wearables and game equipment can provide post-game analysis and feedback and assist in recovery and injury management. Many teams use wearable technology to track players’ movements and collect vital data to monitor their overall health. Types of data that can be collected vary greatly and include breathing and heart rates, speed tracking, hydration levels, wind impact, other environmental factors, and more. This data helps minimize the risk of injury and certain health conditions. It can also provide information about how the athlete performs and where he may be susceptible to injury.
Sensor automation is the driver behind these devices that collect and store the data generated by players. These wearable fitness devices include watches, wristbands, necklaces, and equipment. For example, using sensor automation, AI is being used in golf clubs, balls, baseball bats, and athletic shoes.
As the sports industry becomes more reliant on technology, AI has emerged as a key player in the starting lineup. AI is being utilized to improve the outcomes of games, how they are played, player performance, safety on and off the field, as well as the fan experience.
Now is the time for software providers to enlist skilled digital transformation experts to assist them in developing their sports technology solutions. Collaborating with a skilled AI developer team will ensure that providers of these cutting-edge solutions are leveraging the latest innovative technologies so they may take advantage of a lucrative market and seize a substantial opportunity to help shape the future of sports.
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