olympic-sprinting

This notebook analyzes the performance trends in the men’s and women’s 100-meter and 200-meter dash in the Summer Olympic Games. The goal is to understand:

• How fast each medalists’ time is compared to the model’s expected result for that Olympic year
• Which medalist overperformed their expected result the most compared to all other competitors in the same event
• To predict the expected result for Gold, Silver, and Bronze in the 2024 Olympic Games 100-meter and 200-meter dash

This notebook was forked and based on Omri Goldstein’s work on Kaggle. The updates to his notebook are the following:

• Added results from the Summer 2020 Olympic Games
• Plotted the data and fitted an exponential curve for each medal
• Removed outlier results from early Summer Olympic Games to better fit model
• Predicted the results for the 2024 men’s and women’s 100-meter and 200-meter dash

For a more detailed analysis of this project, you can read my blog post on Medium.

—

data/                 # cleaned results file (results_updated.csv)
notebooks/            # main analysis notebook
reports/
   figures/           # exported PNGs used in the README
src/                  # optional helper scripts
requirements.txt      # dependencies
README.md             # project documentation

pip install -r requirements.txt
jupyter notebook notebooks/ahead_of_their_time_2024.ipynb

• Source: Based on and extended from Omri Goldstein’s Kaggle dataset
• File: data/results_updated.csv
• Fields include:
  • Athlete name
  • Country
  • Event (100m, 200m)
  • Gender
  • Olympic year
  • Medal
  • Final time (seconds)

• Cleaned and extended original results dataset
• Removed early-era outliers and incomplete records
• Fit exponential regression curves for each event
• Computed residuals to identify athletes significantly outperforming the era trend
• Predicted medal-winning times for 2024
• Visualized trends and overperformers

• Sprint performance improves exponentially but has slowed in recent decades
• Certain athletes were dramatically ahead of the curve (especially early years)
• Predicted 2024 medal times using fitted trend lines
• (Optional future update) Compare predictions to actual 2024 results

pandas>=2.1
numpy>=1.26
matplotlib>=3.8
scipy>=1.11
seaborn>=0.13

• Add 2024 actual results to validate predictions
• Add helper functions to src/ for curve fitting and residual analysis
• Extend to 400m or relay events
• Show uncertainty intervals for model predictions

Last updated: 2025-11-14