Staying ahead of the game is crucial for athletes and teams striving for success. Whether for the national game, field hockey, or the beloved sport of cricket, fans and communities in India expect only great things from athletes.
However, injury rates have significantly increased, and barriers to the big sports teams are becoming harder to overcome. To ensure exceptional player performance, prevent injuries, and drive strategic decision-making in sports, sports organizations are turning to technology such as analytics, machine learning (ML), and artificial intelligence (AI).
Preventing injuries through Data & ML
Injuries are common in sports and can have significant consequences. In the last 10-15 years, there have been 500% more knee and ankle injuries and 400% more ACL injuries. Machine learning, a subset of AI that involves training algorithms to learn patterns from data, is a powerful tool that can help predict and prevent athlete injuries.
By analyzing various physical, physiological, social, and psychological parameters, a machine learning algorithm can forecast the likelihood of injury, monitor any signs of fatigue or abnormal movement, and provide guidance for rehabilitation. Examples of such algorithms include decision trees, random forests, and neural networks.
The main component of injury prevention is understanding the risk factors or parameters involved and their interplay. Several parameters can come into play: the athlete’s body condition, player workload data, biomechanical information, previous injury history, external environmental factors, and more. For contact sports, it’s also crucial to consider the key characteristics of the opposing team.
With so many parameters, a comprehensive ML/AI model is crucial. The model must outline the sequence of events leading to injury and describe the body and joint biomechanics at the time of the incident.
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