Digicamp 2025
This project is part of Digicamp 2025, co-teached alongside Amedeo Zappulla, where students are introduced to the fundamentals of data science applied to sports performance.
The focus is on race data analysis, teaching students how to clean, visualize, and interpret performance trends, and finally build a basic predictive model.
Goal
By the end of the course students were able to understand the role of data analytics in sports, perform data cleaning and preprocessing on raw race data, create meaningful visualization to highlight performance trends, apply basic statistical and machine learning techniques to predict outcomes and communicate insights effectively using plots and metrics.
Use case: Track & Field
We worked with track and field race results, in particular we aimed to predict the 100m Olympic final of Paris 2024. Students explored questions such as: how has an athlete's performance changed over time? What trends can we identify in seasonal performance? Can we predict the outcome of a future race using past results?
Tools
We used Python, Pandas (for data manipulation), Matplotlib and Seaborn (for visualization), Scikit-learn (for building simple predictive models) and Jupyter Nootebooks.
