Predication of Final Medal Counts in Olympic Games by Monte Carlo Simulations
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909966313455616 |
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| author | Barker, Maggie Guo, Daniel Palmeri, Justin Shepherd, and Ridge |
| author_facet | Barker, Maggie Guo, Daniel Palmeri, Justin Shepherd, and Ridge |
| contents | In the paper, a program strength model was proposed to evaluate the performance of countries across different Olympic events. The model assessed how strong a country's program was in each event and also factored in the influence of past Olympic performances. The final medal counts from the Paris 2024 Olympic Games were used to validate the model and to determine the optimal set of constants using Monte Carlo simulation. Based on this model, a prediction of the final medal counts for the 2028 Olympic Games is also provides for reference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14798 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predication of Final Medal Counts in Olympic Games by Monte Carlo Simulations Barker, Maggie Guo, Daniel Palmeri, Justin Shepherd, and Ridge Physics and Society 82M31, 90-10, 90C31 In the paper, a program strength model was proposed to evaluate the performance of countries across different Olympic events. The model assessed how strong a country's program was in each event and also factored in the influence of past Olympic performances. The final medal counts from the Paris 2024 Olympic Games were used to validate the model and to determine the optimal set of constants using Monte Carlo simulation. Based on this model, a prediction of the final medal counts for the 2028 Olympic Games is also provides for reference. |
| title | Predication of Final Medal Counts in Olympic Games by Monte Carlo Simulations |
| topic | Physics and Society 82M31, 90-10, 90C31 |
| url | https://arxiv.org/abs/2512.14798 |