Research on Olympic medal prediction based on GA-BP and logistic regression model
Sanglin Zhao1, Jikang Cao1, Keyun Lu1
1School of Engineering Management, Hunan University of Finance and Economics, Changsha, Hunan, China.
F1000Research
|September 29, 2025
Summary
Predicting Olympic medal counts is complex, but a new GA-BP model improves accuracy. The study highlights the significant impact of head coaches on national performance, offering insights for future Olympic strategies.
Area of Science:
- Sports Science
- Data Science
- Predictive Analytics
Background:
- Predicting Olympic medal distribution is a complex challenge.
- Accurate prediction requires considering historical data, athlete performance, and host country factors.
Purpose of the Study:
- To develop and validate a predictive model for Olympic medal counts.
- To forecast the medal table for the 2028 Los Angeles Olympics.
- To analyze the impact of head coaches on national performance.
Main Methods:
- Utilized the GA-BP algorithm model, integrating genetic algorithm (GA) and backpropagation neural network (BPNN).
- Optimized BPNN weights and bias parameters using GA's global search capability.
- Employed a synthetic control model with Estonia and China as case studies.
Main Results:
- The GA-BP model demonstrated improved training efficiency and prediction performance.
- Estonia and China showed increased medal counts when guided by head coaches.
- Estonia's 1992 performance (1 gold, 2 bronze) exemplifies the impact of coaching.
Conclusions:
- Head coaches play a significant role in enhancing athlete and national performance.
- The study offers valuable insights for Olympic Committee decision-making.
- Findings aid in optimizing resource allocation and predicting future Olympic outcomes.
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