## 6 AI and Sports Data  
### Application of AI in Sports Data Analysis  
In this AI-driven era, AI, with its highly intelligent data analysis capabilities, is revolutionizing the way sports are trained. By analyzing the vast amounts of data generated by sports, AI can provide athletes with scientific training programs. Intelligent materials and sensors can collect real-time movement data, such as heart rate, speed, power and posture, to provide the basis for AI analysis. Based on this data, AI can identify an athlete's strengths and weaknesses, optimize training schedules, reduce sports injuries, and enhance competitive performance. For example, smart running shoes and wearables help personalize running training programs by recording stride length, stride frequency and energy expenditure. The smart tennis racket provides technical improvement suggestions by analyzing the swing force and Angle. Ai-driven data sports training programs not only improve training efficiency but also provide more accurate decision support for athletes and coaches and promote sports training into a new era of intelligence and science. By converting various motion states in sports into electrical signals, denoising the signals and transmitting them to computers for data analysis, combined with big data and artificial intelligence technology, the tactics and motion states in sports can be effectively analyzed. At the same time, by combining the individual data of each athlete into a large data set, through AI learning and big data analysis mode, tactical formulation, training effect prediction, and other functions can be realized. The introduction of this technology has obviously promoted the development of sports training towards a new data-driven model. For example, in 2021, [PERSON] et al. (2021) reported a machine learning-based algorithm is introduced to use an inertial measurement unit (IMU) to evaluate the orientation of body parts and obtain accurate estimates of the orientation of body parts. The experimental results show that the estimation accuracy of the instantaneous trunk inclination can be significantly improved in sports occasions such as wheelchair sports, where IMU is difficult to use. Therefore, this algorithm has important application value for evaluating the orientation of body parts in difficult IMU situations. Figure 14: An intelligent badminton racket with the ability to sense and collect information such as the force position of the badminton shuttlecock (2021). In 2022, [PERSON] et al. [82] reported that the psychological state of athletes was detected through wearable devices and machine learning techniques. A combination of coach observation and machine learning was used to collect inertial measurement unit data from tennis players and train a recurrent neural network to predict the psychological state label given by the coach. The experimental results show that this method can accurately predict the optimal psychological state and provide new ideas and technical support for the field of sports. In 2024, [PERSON] et al. [83] reported that a comprehensive analytical framework based on the BP NN model was developed to analyze the factors affecting the performance of elite skaters. Eight high-speed skaters from the Chinese national team were selected, and data from a total of 403 games were analyzed between 2013 and 2022. The results showed that the altitude of the ice rink had a positive impact on the performance of all athletes, and at the same time, the increase in the frequency of games also helped to improve the performance level. These research results provide important guidance for speed skaters and coaches, helping to optimize their training and competition strategies and ultimately improve their competitive level. In 2024, [PERSON] et al. [84] report discussions that AI can be applied to performance improvement, health care, technical and tactical support, talent identification, game prediction, business growth, and other aspects in competitive sports.