Revolutionizing Sports Analytics: Unveiling Key Determinants of Olympic Success through Statistical Modeling and Innovative Technologies

This study investigates improvements in sports analytics, with an emphasis on the Olympics, and uses statistical tools to identify significant performance determinants. The study uses Kaggle's athlete dataset to evaluate the impact of demographic and physical variables such as age, height, weight, and gender on medal success. Statistical procedures such as T-tests, Chi-square tests, and ANOVA indicate substantial differences between groups, including gender-specific patterns and differences in Summer versus Winter sports performance. Prior studies included limitations such as inadequate data, biases, and the inability to analyze underrepresented sports. The study also offers a sports suggestion app that uses augmented reality and wearable devices to provide personalized and immediate recommendations that adjust to environmental and individual variables. The methodology combines hypothesis testing, data preprocessing with IBM SPSS, and visualizations to derive actionable insights. Key findings include gender inequalities, with males averaging higher height (177.84 cm vs. 168.51 cm) and weight (74.43 kg vs. 61.14 kg), as well as seasonal variances, such as age differences between summer and winter sports. Medallists revealed distinct traits, with gold medallists standing at an average height of 177.28 cm and weighing 73.44 kg.