The objectives of this project are to design a statically determinant load cell mechanism for a prototype tow tank ultimately culminating in the testing of the aerodynamic performance of a Formula One racing car model. This paper also serves as a proof of concept for force data collection for a full-sized tow tank being developed by Isabella All [8]. The project includes the design and construction of the load cell mechanism which utilizes a load cell to measure the force in a specific member of the mechanism which is then used to determine the semi-lift and drag forces for a given test model. For this specific project, a model of the front-end of an F1 racing car was used for data collection and analysis. It was found that for a short period of time within each test run, constant force data was able to be collected from the load cell which could then be transformed into semi-lift and drag force data. Ultimately, the drag coefficient acting on the model was found to be in the range of 0.9 to 1.3 which somewhat falls in line with the estimated values of 0.7 to 1.0 [1] for F1 racing vehicles. Although the final data collected may not be entirely accurate due to errors discussed in the paper, the ideas presented in this project can be fully realized with some minor changes and adjustments.

Sports analytics refers to the implementation of data science and analytics techniques within the sports industry. Several sports analysts and team managers have utilized analytical tools to boost overall team and player performance, often through the analysis of historical data. One of the most common techniques employed in sports analytics is that of data mining–the extensive practice of analyzing data in order to extract and deliver insights and findings. Data mining projects are frequently guided with the six-step Cross Industry Standard Process for Data Mining (CRISP-DM) framework. One such sport that has extensively used data science and analytics, and data mining specifically, is that of Formula One (F1). Given the sports’ reliance on technology, race engineers working for F1 constructors often develop statistical models analyzing historical race performance to derive insight of drivers’ success. For the purposes of this project, the perspective of a race engineer working for the F1 constructor McLaren was considered. As the constructor is seeking to gain a competitive advantage for the upcoming F1 season, race performance data concerning previous seasons was collected and analyzed as part of a larger data mining project utilizing the CRISP-DM framework. Statistical models, such as linear regression and random forest, were developed to predict the number of points scored by McLaren racers and the variables most strongly contributed to such scored points. The final results point to specific lap times having to be aimed for as the most important variable in determining the number of points gained, although specific locations also seem prone to McLaren race success. These results in turn will be utilized to develop race strategies for the upcoming season to ensure McLaren has high efficiency against its competitors.