Olympic Medal Forecasting and Coaching Impact

This study employs multi-model frameworks to analyze medal distribution and influencing factors for the 2028 Los Angeles Olympics.A K-means clustering algorithm categorizes participating nations, while an LSTM neural network integrates event quantity, athlete participation, and home advantage to predict medal allocations (test set MAE=0.0425, MSE=0.0358,R=0.9238), identifying evolutionary patterns in medal standings.Random Forest modeling predicts emerging nations' first-medal probabilities (SYR/IRL odds=1.0)with validation metrics MAE=0.1787 and R=0.8217.The entropy weight method establishes sport-specific significance indices to optimize resource allocation.Analytic Hierarchy Process (AHP) quantifies the "Great Coach" effect through decision matrices (total medals weight=0.5396,gold medals=0.1634,participation scale=0.2970;CI=0.0046,CR=0.0088), highlighting France's leadership in artistic swimming (diversity factor=1.000),Italy's beach volleyball excellence (0.7389), and U.S. track & field dominance (0.5319).Feature importance analysis identifies historical medal counts, active athlete numbers, and competitive experience as core predictors, confirming coaches' professional expertise significantly enhances athletic performance (p<0.01).These findings provide quantitative evidence for Olympic committees to refine training strategies and resource distribution.