To address national total medal count prediction and performance evaluation, within the context of increasing techno logical competition in sports equipment, a Bivariate-Hurdle-Tobit composite model was constructed. This model is suitable for predicting zero-inflated, count- type, and highly correlated dependent variables. The model recognizes that a nation's athletic success is increasingly underpinned by its industrial and technological prowess, including advancements in textile engineering for. Through factor analysis and correlation tests, six significant regression factors were identified: total athlete count, host status, participation rate in dominant events. The model underwent comprehensive evaluation via rolling cross- validation and five metrics, demonstrating low prediction error and high accuracy with AUC values reaching 0.98 and 0.96 respectively, and Pseudo R² consistently exceeding 75%. Predictions indicate the United States, China, and the United Kingdom will occupy the top three positions on the medal tally. The study reveals a significant positive impact of the "host nation effect" on national performance. For instance, the U.S. as the next host nation is projected to increase its gold medal share by 0.75%. Additionally, countries like Andorra, Benin, and Belize are predicted to have a 95% probability of winning their first-ever medals. Analyzing the influence of great coaches, the study quantifies the "great coach effect" using Chow tests and Difference-in-Differences models. Findings indicate this effect has limited impact in non-host nations but exhibits significant synergistic effects in host countries. For additional insights into the Olympic medal standings, the model incorporates gender ratio analysis. Results show male athlete participation rates have a significant negative impact on medal distribution, suggesting an increase in female athlete representation is advisable.

high performance sports textiles, bivariate hurdle, tobit, stepwise regression, difference in differences