Building the Toolkit: Tech Stack and Tools

Much of the development work was carried out using Python and its associated libraries. Google Colab served as the primary development environment due to its accessibility, cost-effectiveness, and integration with other tools and platforms.

Why Python?

Python is one of the most widely used languages for data analysis, due to its veratililty, readability and simplicity. It offers an extensive ecosystem of modules, both custom and community built and frameworks for data extraction, modelling, and analysis. Python is also heavily use within data science and AI frameworks. Using python would enable seamless intergration between systems and processes.

Modules used

Python’s power in data analysis comes from its rich set of libraries. Pandas is used for handling and manipulating tabular data efficiently. FastF1 is a specialized library for accessing and analyzing Formula 1 telemetry and timing data. It provides a simplified interface to the OpenF1 API, allowing users to retrieve detailed race information such as lap times, sector data, and car telemetry without needing to manually construct and manage API calls. FastF1 was used for its ease of use—it abstracts away much of the technical setup and delivers preprocessed, structured data ready for analysis. It also includes built-in caching, integrates smoothly with visualisation tools like Matplotlib and Seaborn>, and benefits from strong community support. However, if real-time data or more customized queries are needed, the OpenF1 API can be accessed directly to provide more granular control. Open-source resources for FastF1 can be found on GitHub. Utility modules such as os and datetime assist with file handling and time-based operations, while logging supports best practices in debugging and code monitoring.

Google Colab

Google Colab provides a cloud-based environment that allows you to write and execute Python code from anywhere with an internet connection. It removes the need for local installations and integrates seamlessly with Google Drive for easy data access and sharing. Colab also offers GPU and TPU acceleration for computationally intensive tasks, making it ideal for both data analysis and machine learning workflows. However, these were not used as part of this analyis. Additionally, it integrates effectively with Google Gemini and other AI tools, enabling enhanced analytical capabilities and AI-assisted insights directly within notebooks.