Since 2021, I've participated in an F1 fantasy league with friends and family. For those unfamiliar, F1 fantasy is a game where you create a team of Formula 1 drivers and teams, earning points based on their race weekend performance. Being a data enthusiast, I wanted to see if I could make more data-driven decisions for my team, so I set out to do just that.

I discovered a fantastic data source called Ergast, a public API that provides access to F1-related data dating back to the 1950s. Using Python, I built a connection to this API, and that's where the fun began.

Here's the process I followed:

1. Set up a data warehouse: To store and model the data.

2. Automate data updates: Download and incorporate new data after every race weekend.

3. Create a semantic model: To effectively structure and interpret the data.

4. Develop a machine learning model: To predict drivers' finishing positions based on historical data.