To execute a task that individuals have trouble performing, machine learning algorithms are applied. In both league and practice schedules, the study and prognostication of the success of particular fitness events by athletes are increasingly relevant. When employing traditional approaches, the variety and difficulty of particular forms of sporting events and the often time-varying interactions among them start making research and forecasting activities difficult. The utilization of powerful machine learning (ML) algorithms can analyze the athletic success of players with amazing precision. The goal of this analysis was to test various ML models to forecast unique varieties of player`s success and to use a better system to decide the critical attributes affecting projected outcomes in woman handball athletes. The basic type of regression in ML, i.e., simple linear regression (SLR), classification tree (CT), support vector regression (SVR), neural networks that employ radial basis function (RBFNN), was executed to forecast the potential abilities of women handball players in squat jump (SJ), squat jump on toes (SJT), sprint over a 10-m distance(SP10), and a handball sport-skill test (HSST). To every ML model, a maximum of 23 feature values and 117 occurrences of training samples were captured. The outcomes proved that the RBFNN performed better than other models and was efficient in forecasting the player`s performance with R-squared values between 0.86 and 0.97. We also evaluated all the models using other performance metrics like mean squared error (SE) and mean absolute error (AE). Lastly, by upskilling the superlative system, essential attributes affecting expected success were evaluated. This is the initial and earliest attempt using ML in the field of sports, i.e., handball, and the findings are promising and appealing for subsequent researchers.
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