International Journal For Multidisciplinary Research

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Revolutionizing Sports Excellence through Machine Learning

Author(s) Arjun PV, Pramod K
Country India
Abstract In the constantly changing world of sports, where the difference between winning and losing is very small, the persistent efforts and drive for a competitive edge have led to the fusion of advanced technologies and analytical methods, with the use of machine learning algorithms highlighted as a revolutionary method among these innovative approaches in the domain of analyzing player performance. This seminar acts as a guiding light, illuminating the possibilities for transformation and exploring machine learning from a new angle, examining its various applications in studying athletes thoroughly, enhancing athletic performance through tailored training programs, and influencing strategic decision-making in sports management, thus improving interaction with supporters. Machine learning, with its remarkable ability to analyze huge amounts of data and identify patterns in complex designs, provides a unique glimpse into the intricate world of player performance. By using cutting-edge algorithms, sports associations can reveal new perspectives on player data that were previously unattainable, from physical characteristics to strategic tendencies, ultimately achieving a thorough understanding that goes beyond conventional analytical techniques. Analyzing biometric data during trainings and reviewing match footage, machine learning provides coaches, analysts, and medical staff with strategic knowledge to make data-driven decisions that lead to success and improvements in performance and risk reduction. In addition, the inclusion of machine learning methods in sports goes beyond simply evaluation of performance, driving the industry towards predictive analytics. Machine learning has the ability to make predictions, enabling teams to predict how players will act, anticipate opponent strategies, and forecast injuries with a level of accuracy never seen before. This new change in sports analytics gives decision-makers the power to improve game strategies and also benefits athletes, providing individuals with the resources needed to perfect their abilities and maximize their performance on the field, court, or track. Additionally, artificial intelligence practice transforms the process of identifying and recruiting players by analyzing large amounts of data to discover hidden talents and make suitable matches with requirements from the team being integrated smoothly, using predictive analytics to help teams in talent acquisition make well-informed decisions and improve their optimization efforts, creating a team with the right mix of members to achieve maximum performance. Not only does it affect performance analysis and talent recruitment, but machine learning also impacts fan involvement and enjoyment by providing personalized interactions, customized content suggestions, and engaging experiences. By examining how fans act and what they prioritize, sports organizations can develop distinct fan interactions, boosting engagement and commitment. As we embark on this adventure to investigate the merging of machine learning and sports performance analysis, we are destined to discover more than just the great opportunities, but also the complex challenges and moral concerns that come with this technological revolution. In the end, this workshop seeks to lay the groundwork for a new era of outstanding athleticism, by using data-driven analysis and innovative ideas to push sports to new levels, transforming the fundamental nature of competition and fan involvement with accomplishments and enthusiasm.
Keywords Machine learning algorithms, Fan involvement, Player performance analysis, Predictive analytics, Talent recruitment
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-06
Cite This Revolutionizing Sports Excellence through Machine Learning - Arjun PV, Pramod K - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.16581
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.16581
Short DOI https://doi.org/gtp8hq
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