Sports
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- Case Studies
- Dutch sports analytics company SciSports uses emerging tech to innovate on the pitch | SAS
- AI Is Changing The Face Of Golf Club Design | Steve McCaskill
- FIFA to install AI to help make accurate offside decisions | Juana Summers - NPR
- The NFL and Amazon are using AI to invent new football stats | Malak Saleh - Engadget ... “Next Gen Stats” can give a breakdown of individual player movements on a field.
Artificial Intelligence (AI) has become increasingly prevalent in the field of sports, revolutionizing various aspects of the game. From player performance analysis to fan engagement, AI has the potential to enhance decision-making, training, and overall sporting experience. Researchers and practitioners are actively exploring and implementing AI techniques to gain valuable insights and optimize performance in sports. Ongoing research and development in this field will continue to push the boundaries of sports analytics and contribute to improved performance and enhanced experiences for athletes and fans alike.
Player Performance Analysis:
- AI plays a vital role in analyzing and improving player performance. Advanced computer vision algorithms can process vast amounts of video footage to extract relevant data and metrics. These algorithms can track player movements, identify patterns, and provide valuable statistics for performance evaluation.
- Machine learning models can analyze large datasets of player and team statistics, training regimes, and match performance to identify patterns and correlations. This analysis helps coaches and trainers make data-driven decisions regarding player selection, tactics, and strategies.
- Researchers have also been exploring the use of AI to predict injuries by analyzing various factors such as player workload, movement patterns, and biometric data. By identifying potential risk factors, teams can proactively manage player fitness and reduce the occurrence of injuries.
Game Strategy and Decision-making:
- AI-powered algorithms can analyze historical match data, player statistics, and opponent performance to generate insights for game strategy. Coaches can use these insights to devise effective game plans, including optimal lineup selection, substitutions, and in-game tactical adjustments.
- Real-time data analysis using AI can provide coaches and players with immediate feedback during matches. For example, AI systems can analyze player positioning, movement, and performance in real-time, enabling coaches to make informed decisions and adjustments during games.
Fan Engagement and Viewing Experience:
- AI has the potential to enhance the fan experience by personalizing content and improving engagement. AI-powered recommendation systems can analyze user preferences, behavior, and historical data to suggest relevant content, such as highlight reels, player profiles, and match analyses.
- Virtual and augmented reality technologies combined with AI can create immersive experiences for fans. AI algorithms can generate realistic simulations and virtual environments, allowing fans to experience the game from different perspectives or participate in interactive virtual tournaments.
- AI-powered chatbots and virtual assistants can provide instant responses to fan queries, deliver real-time updates, and offer personalized recommendations. This technology enhances fan engagement and provides a seamless experience for users.
Research and Development:
- Researchers are continuously exploring novel AI techniques to improve sports analytics. This includes developing advanced machine learning algorithms, deep learning models, and computer vision systems specifically designed for sports applications.
- AI research in sports also focuses on developing predictive models for outcomes such as game results, player performance, and injury probabilities. These models can assist teams in making informed decisions and optimizing performance.
- The integration of AI with wearable devices, such as smartwatches and fitness trackers, enables real-time monitoring of player performance and health parameters. This data can be analyzed to provide personalized training programs and optimize player performance.
Baseball Analytics
Baseball - Stealing Signs
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Skeletal Motion
Fujitsu’s AI Gymnastics: The joint recognition module uses deep learning technology. The neural network model receives several multi-viewpoint depth images as input and outputs corresponding 3D joint position results. Joint recognition also requires certain adjustments and calibrations according to the human joint model, however the method behind this has not been published. A Fujitsu researcher told Synced his team worked closely with the Japanese Gymnastics Association to build up the database with a set of elements for each skill difficulty. They collected over 800 elements for male gymnasts and more than 500 elements for female gymnasts — where the elements comprise a series of basic skills from top gymnasts. Again, details on the procedure and the possible use of deep learning remain cloaked in mystery. Meet Fujitsu’s AI Gymnastics Judges
