Impact of velocity and impact angle on football shot accuracy during fundamental trainings

The purpose of this research is to create a machine learning-based smart coaching approach for football that can replace manual analysis with real-time feedback for trainers. In-depth analysis of football player data by humans is time-consuming, error-prone, and requires a lot of effort. This exploratory study demonstrates the feasibility of using a machine learning algorithm to enhance the effectiveness of player monitoring and training. The suggested approach uses machine learning to generate analytical insights and enable long-term monitoring of player performance. In the future, machine learning could use this technique to offer constructive criticism of football players. The system incorporates a homemade ball-throwing mechanism capable of launching the ball in a variety of directions and at varying velocities. The ball kicker is equipped with a gyroscope and accelerometer sensors for measuring velocity and acceleration. The gathered data is filtered initially, and then the data that has been processed is fed into the machine-learning algorithm. The algorithm will be trained on player performance data and will be able to provide real-time feedback to coaches on player performance and potential areas for improvement. Additionally, the system will be able to track player progress over time and provide coaches with a comprehensive view of player development. The ultimate goal is to improve player performance and reduce the workload for coaches by automating the analysis process.