Exploring Swimming Performance with Data

Thrilled to share my first steps in predicting swimming performance using data!

We know athletes employ unique pace strategies, and long-distance swimmers often surge at the start and finish. My initial exploration used a Random Forest model to see if we could predict final times based on preliminary rounds data and individual pace from 5 Olympic Games and 174 races.

The results are promising! The model learned effectively from the training data, explaining over 95% of the variance in times. While still in its early stages, the model predicted final times with an average error of just 5.7 seconds – less than 0.58% of the entire race!

While this is an initial exploration, it reveals the potential of data-driven models like Random Forest to optimize swimming performance.

Next steps: gather more data, explore advanced techniques, and refine the model for even sharper predictions!

Stay tuned for further dives into the world of data and swimming!

#datascience #sportsanalytics #swimming #performance #exploration #linkedin World Aquatics

P.S. Share your thoughts and questions in the comments! Let's discuss how data can revolutionize swimming!