Data analytics and visualization in Formula 1: a desktop and web-based system for analyzing 2024–2025 season performance
El Founti Khsim, Omar (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025123039016
https://urn.fi/URN:NBN:fi:amk-2025123039016
Tiivistelmä
This thesis presents the development of a two-part system for analyzing and visualizing performance data from the 2024–2025 Formula 1 season, combining a Java and MySQL desktop application with a Python-based web interface. The system was designed with a strong emphasis on clarity and structured data analysis, enabling users to explore pilots, team, and circuits performance in a clear manner.
The results of the thesis confirm the initial hypothesis that applying alternative data analysis and visualization methods to structured datasets significantly improves the understanding of competitive performance in motorsport. The results show that the platform can visualize important trends, such as variations in performance between different circuits, changes in consistency throughout the season, and differences in how teams and pilots evolve over time. Users can easily track race results, championship standings, and point distributions.
The desktop application, deployed using Clever Cloud, provides a stable structure for managing and querying the data, while the Streamlit-based web dashboard offers an interactive environment where users can explore the results in a more intuitive way. Also, a major outcome of the thesis is the development of the F1 Legends League, an interactive fantasy feature that allows users to create custom teams under budget constraints.
Overall, the system successfully achieves its objectives by combining reliable data management with clear visual presentation, allowing complex performance data to be understood more easily and presented in a more engaging way.
The results of the thesis confirm the initial hypothesis that applying alternative data analysis and visualization methods to structured datasets significantly improves the understanding of competitive performance in motorsport. The results show that the platform can visualize important trends, such as variations in performance between different circuits, changes in consistency throughout the season, and differences in how teams and pilots evolve over time. Users can easily track race results, championship standings, and point distributions.
The desktop application, deployed using Clever Cloud, provides a stable structure for managing and querying the data, while the Streamlit-based web dashboard offers an interactive environment where users can explore the results in a more intuitive way. Also, a major outcome of the thesis is the development of the F1 Legends League, an interactive fantasy feature that allows users to create custom teams under budget constraints.
Overall, the system successfully achieves its objectives by combining reliable data management with clear visual presentation, allowing complex performance data to be understood more easily and presented in a more engaging way.