For those primarily interested in the insights alone, I recommend reading IFSC_Abridged_Report.pdf.
For those interested in the details of the data cleaning and modeling design, I recommend reading IFSC Classification Report.pdf.
In the upcoming Paris 2024 Olympic Games, competitive rock climbing will be an official event. This report documents the development of a 95.6% accurate supervised machine learning models to predict whether a given rock climber should be classified as a contender to win an International Federation of Sport Climbing (IFSC) bouldering world cup based data taken from 2018 and 2019 IFSC bouldering competitions. Given the similarity between the IFSC competition structure and that of the Olympic Games, being able to accurately predict the likely winners of IFSC events will help countries select their team members. The models covered here could also be implemented to set betting odds for IFSC or Olympics bouldering events.
- September 2022: The full report is released. The raw code, RStudio, and PDF formats are available at launch.
- November 2022: An abridged report is released. It summarizes the key findings, retains all visualizations, and discusses the potential impact of the optimal model.
This project is currently marked as complete and no further work is expected at this time. Please feel free to make use of any part of the code included in the repo. If you are using any of the text (non-code) content, please cite me (ideally by including a link to this repo).
I make no claim to the data used. It is originally sourced from: https://www.kaggle.com/datasets/brkurzawa/ifsc-sport-climbing-competition-results.