|Year : 2023 | Volume
| Issue : 1 | Page : 7-9
Role of predictive modeling and personalized modeling in the enhancement of athletic performance
Mohammad Ahsan1, Md Dilshad Ahmed2, Kaukab Azeem3
1 Department of Physical Therapy, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
2 Department of Humanities and Social Sciences, Prince Mohammad Bin Fahd University, Dammam, Saudi Arabia
3 Department of Physical Education, College of Medicine, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
|Date of Submission||05-Jun-2023|
|Date of Decision||08-Jul-2023|
|Date of Acceptance||09-Jul-2023|
|Date of Web Publication||07-Aug-2023|
Department of Physical Therapy, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam
Source of Support: None, Conflict of Interest: None
In sports, the difference between winning and losing can often come down to the smallest of margins. As such, athletes and coaches are constantly looking for ways to gain an edge over their competitors. One area that has seen significant growth in recent years is the use of predictive modeling and personalized modeling to enhance athletic performance. Predictive and personalized modeling helps athletes and coaches make more informed decisions about training and preparing for competitions. By identifying the areas of weakness or predicting potential injuries, these models can help athletes take proactive steps to address these issues. Predictive and personalized modeling in sports is also associated with challenges. Despite the challenges, the potential benefits of predictive and personalized modeling in sports are clear. By providing athletes with highly targeted insights into optimizing their performance, predictive modeling and personalized modeling help them to achieve their full potential and reach new heights in their respective sports. As a result, it is expected that there will be further increases in the implementation of predictive and personalized modeling in the forthcoming years.
Keywords: Artificial intelligence, injury prevention, machine learning, personalized modeling, predictive modeling, sports performance
|How to cite this article:|
Ahsan M, Ahmed MD, Azeem K. Role of predictive modeling and personalized modeling in the enhancement of athletic performance. Saudi J Sports Med 2023;23:7-9
|How to cite this URL:|
Ahsan M, Ahmed MD, Azeem K. Role of predictive modeling and personalized modeling in the enhancement of athletic performance. Saudi J Sports Med [serial online] 2023 [cited 2023 Sep 29];23:7-9. Available from: https://www.sjosm.org/text.asp?2023/23/1/7/383100
| Introduction|| |
Predictive modeling and personalized modeling are two cutting-edge technologies that significantly impact the world of sports to enhance athletic performance. Predictive modeling uses historical data to predict future outcomes, whereas personalized modeling uses data to create customized plans for athletes. Predictive modeling can be used to predict an athlete's performance in a future event. It can be used to help the athlete train more effectively and make better decisions about their preparation. For example, a predictive model could be used to predict how an athlete's performance will be affected by changes in weather conditions or by the presence of a particular opponent. Personalized modeling can be used to create customized training plans for individual athletes. These plans can be tailored to the athlete's specific needs and goals and updated as their performance improves. For example, a personalized training plan could include strength training, cardio training, and skill development exercises. Both predictive modeling and personalized modeling can be used to improve athletic performance.
| Predictive Modelling|| |
Artificial intelligence (AI) is used for predictive modeling in sports, allowing for the computation of the probability of win/loss prospects and predicting athletic performance. AI implementation for predictive modeling in sports has been observed through various means. Predictive models in sports can compute probabilities of win/loss prospects, allowing teams to optimize their strategies. A prediction model of sports performance based on a neural network algorithm can accurately process and predict data. In 2016, the New England Patriots used predictive modeling to help them win the Super Bowl. The Patriots used data from previous games to predict the likelihood of different plays being successful. This information helped them make better decisions about which plays to call, ultimately leading to their victory. Machine learning is used to predict athletic performance from physiological parameters, allowing for the identification of high-potential athletes in advance. The National Football League (NFL) uses predictive modeling to identify players at risk of concussions. This information is then used to provide the players with additional safety equipment or modify their playing style. AI-powered tracking systems capture detailed data on players' movements, including their speed, acceleration, and distance covered, which can be used to monitor player performance during training and games and predict future performance. The NFL uses predictive modeling to identify players at risk of concussions. This information is then used to provide the players with additional safety equipment or modify their playing style. By analyzing vast amounts of data on players' performances, AI identifies patterns and trends, providing insights into individual players' and teams' strengths and weaknesses. This is used to optimize training programs and predict future performance. AI transforms how teams and coaches predict and optimize their strategies, allowing for more informed decisions and improved performance.
Sporting organizations employ diverse methods to gather and administer the requisite data for predictive modeling. Sports teams employ various methods to gather and handle the data. Predictive analysis websites such as Sportsline.com and simulation models developed by sports data science experts are used to predict game outcomes. The science of rating and ranking is covered in detail, and regression models are used for estimating a metric from several predictor variables. Teams compute simple statistics of games that have already been played, then use correlation to detect statistical relationships between game metrics. Teams use Python and logistic regression as a way of modeling game results using data on team expenditures. Teams use sabermetrics to evaluate teams and players, using metrics such as on-base percentage, slugging percentage, and Wins Above Replacement. Sports teams use statistical analysis and machine learning techniques to collect and manage the data needed for predictive modeling, allowing them to optimize their strategies and predict future performance.
| Personalised Modelling|| |
The sports industry is undergoing a significant transformation due to the rapid advancement of AI, with the development of personalized modeling for sports performance emerging as a particularly promising application. Using AI in personalized modeling enables the analysis of a diverse data set, encompassing an athlete's physiological and psychological characteristics, training background, and performance outcomes. Subsequently, these data are utilized to generate a customized training program specifically designed to cater to the distinct requirements and objectives of the athlete. In 2018, Serena Williams used personalized modeling to help her Win her 23rd Grand Slam title. Williams worked with a team of data scientists to create a customized training plan tailored to her individual needs and goals. This plan helped her stay healthy and improve her performance, ultimately leading to her victory. The Australian Institute of Sport uses personalized modeling to create training programs for athletes.
The programs are tailored to each athlete's needs, such as their physical attributes, training history, and competition results. There are several benefits to using personalized modeling for sports performance. First, it can help athletes to improve their performance more quickly and efficiently. By targeting training specifically to an athlete's strengths and weaknesses, personalized modeling can help them to reach their full potential. Second, personalized modeling can help to reduce the risk of injury. By identifying and addressing potential risk factors, personalized modeling can help athletes to stay healthy and on the field. Third, personalized modeling can help athletes to stay motivated. The United States Olympic Committee uses personalized modeling to create training programs for athletes. The programs are tailored to each athlete's needs, such as their physical attributes, training history, and competition results. By providing a clear and achievable plan, personalized modeling can help athletes to stay focused and on track.,
Many companies are developing personalized modeling solutions for sports performance. Several prominent companies and applications are widely recognized, including Catapult, a sports technology company that provides wearable sensors that track athletes' movement and performance data. Professional teams use Catapult's data in various sports, including football, rugby, and cricket. StatMuse is a sports data company that provides AI-powered insights and predictions for sports fans and bettors. StatMuse's data are used by fans to make more informed decisions about their bets and by teams to improve their performance. Hudl is a sports video analysis company that provides tools for coaches and athletes to analyze video footage of games and practices. Coaches use Hudl's data to identify the areas for improvement and for athletes to learn from their mistakes. Personalized modeling is still a relatively new technology, but it can potentially revolutionize how athletes train and perform. Future uses for AI are likely to be even more cutting-edge and inventive as it develops.,
| Conclusion|| |
As predictive and personalized modeling technologies continue to develop, there are anticipated to be further pioneering and innovative applications. Implementing these technologies has the potential to bring about a significant transformation in the methods of athletic training and performance, thereby facilitating the attainment of optimal athletic potential. It is necessary to acknowledge that these instruments do not replace persistent effort and commitment. In order to attain their utmost capabilities, athletes should commit themselves to rigorous training and competition.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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