F1 data analysis and tactical insights : exploring Formula 1 race performance strategies
Msakamali, Baraka (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024051712928
https://urn.fi/URN:NBN:fi:amk-2024051712928
Tiivistelmä
The purpose of the study was to investigate the complexities of analyzing Formula 1, and to examine the methods and strategies employed by various teams. By utilizing both the Fast F1 and Anaconda frameworks, pre-processed data was accessed and analyzed to reveal valuable insights into race dynamics and the implementation of strategies. The study encompassed two case studies to accomplish the intended objective.
Two case studies were carried out, one concentrating on telemetry analysis and the other on tire performance. The objective was to examine the influence of analysis metrics on race performance and strategies. The results indicate that although the analysis may not provide precise predictions, it does have a notable impact on distinguishing various team strategies.
The study underscores the need for practitioners in Formula 1 data analytics to gain a deeper understanding of the technologies utilized and improve analysis planning to enhance the quality of the visual representations of race dynamics and performance metrics. This thesis can contribute to the ongoing evolution of Formula 1 data analytics, offering valuable insights for teams, researchers, and enthusiasts seeking to enhance their understanding of race dynamics and optimize performance on the track.
Two case studies were carried out, one concentrating on telemetry analysis and the other on tire performance. The objective was to examine the influence of analysis metrics on race performance and strategies. The results indicate that although the analysis may not provide precise predictions, it does have a notable impact on distinguishing various team strategies.
The study underscores the need for practitioners in Formula 1 data analytics to gain a deeper understanding of the technologies utilized and improve analysis planning to enhance the quality of the visual representations of race dynamics and performance metrics. This thesis can contribute to the ongoing evolution of Formula 1 data analytics, offering valuable insights for teams, researchers, and enthusiasts seeking to enhance their understanding of race dynamics and optimize performance on the track.