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Exploration of Olympic Medal Distribution: A Combined Application of BP Neural Network and Statistical Methods

Author(s)

Ziyu Gao, Yonggang Guo, Zhifeng Chen, Ziyan Cui, Qizheng Zhang

Affiliation(s)

Shijiazhuang Tiedao University, Shijiazhuang, Hebei, China

Abstract

This paper examines Olympic medal distribution using various models and methods to provide decision-making insights for the International Olympic Committee and national sports bodies. In Question 1, a BP neural network model predicts 2028 medal counts and changes per country, incorporating factors like athlete participation, gender ratio, home-field advantage, and sports types, while estimating first-win probabilities for “0 prize” countries after addressing missing values and outliers. Correlation analysis then links sports to medals, e.g., Japan’s edge in judo and wrestling. For Question 2, the BP neural network and mathematical statistics identify “great coach” effects in countries like Japan and Mexico, with sensitivity analysis pinpointing investment-worthy sports, such as judo and gymnastics in Japan. Question 3 summarizes medal distribution patterns from prior models, offering the IOC resource allocation and strategy recommendations. The study’s multi-factor model ensures high accuracy, identifies key influences, and suits all country types. Future dynamic modeling with real-time data could boost robustness. Results optimize sports resource allocation and inform data-driven decisions in finance, healthcare, and beyond.

Keywords

Prediction and Optimization of Olympic Events; BP Neural Network Models; Correlation Analysis; The “Great Coach” Effect; Mathematical Statistics

References

[1] Otamendi J ,Doncel M L . Medal Shares in Winter Olympic Games by Sport: Socioeconomic Analysis After Vancouver 2010 [J]. Social Science Quarterly, 2014, 95 (2): 598-614. [2] Alan V, David H. Prevalence of therapeutic use exemptions at the Olympic Games and association with medals: an analysis of data from 2010 to 2018.[J]. British journal of sports medicine, 2020, 54(15): 920-924. [3] L. N S .Twin Types: Variations and Common Themes/Twin Research Reviews: Birth Weight and Brain Development; Twinning and Vocabulary Knowledge; Fetal Loss in Twin Pregnancy; Twin-Family Olympic Medal Winners/Human Interest: Young Twin Soldiers; Twin-Run Laundry; Male-Female Pair and Politics; Twin-Based Graphic Novel; Twin Sisters’ Deliveries[J]. Twin Research and Human Genetics, 2021, 24(1): 89-94. [4] Yassin K ,Danilo M, Ervin S .A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals[J]. Information Fusion, 2021, 6952-72. [5] Zhiwei G , Lianggui T , Tan G , et al. Deep Graph neural network-based spammer detection under the perspective of heterogeneous cyberspace [J]. Future Generation Computer Systems, 2021, 117205-218. [6] Zhuhuang Z , Anna G , Weiwei W , et al.Parameter estimation of the homodyned K distribution based on an artificial neural network for ultrasound tissue characterization [J]. Ultrasonics, 2021. [7] Wang X ,Yao Y ,Li Z , et al. Protocol Reverse Analysis of Ethernet for Control Automation Technology Based on Sequence Alignment and Pearson Correlation Coefficient [J]. Sensors, 2024, 24(24): 7922-7922. [8] Helmy R ,Steven B , Augie W , et al. Image encoding selection based on Pearson correlation coefficient for time series anomaly detection [J]. Alexandria Engineering Journal, 2023, 82304-322. [9] Zoltan D. Use one system for all results to avoid contradiction: Advice for using significance tests, equivalence tests, and Bayes factors.[J]. Journal of Experimental Psychology: Human Perception and Performance, 2024,50(5):531-534.