## Ferrari's F1 Performance and Market Value: A Quantitative Analysis This essay analyzes the correlation between Scuderia Ferrari's Formula 1 (F1) performance and the market value of Ferrari's road car division. We'll examine periods of dominance and struggle, using mathematical tools like correlation coefficient, linear regression, and arithmetic sequences to quantify the impact of on-track results on Ferrari's financial performance. Data limitations exist, particularly for Ferrari's pre-1969 financial records due to its private company status. Post-1969 analysis will incorporate Fiat's financial reports, with Ferrari's public listing in 2015 providing the most readily available data. ### Case 1: 2009 vs. 2010 – The Post-Schumacher Era This case compares Ferrari's performance and financial results in 2009 (a struggling year post-Michael Schumacher) and 2010 (a year of improved performance with Fernando Alonso). **Data:** | Year | Wins | Revenue (million Euros) | Operating Profit (million Euros) | Cars Sold | |---|---|---|---|---| | 2009 | 0 | 1,778 | 245 | 6,193 | | 2010 | 5 | 1,919 | 302 | 6,573 | **Analysis:** We'll use the correlation coefficient formula to assess the relationship between Ferrari's wins and revenue. The formula is complex and will not be fully shown here, but the result is crucial. The correlation coefficient ranges from -1 to +1. A value close to +1 indicates a strong positive correlation, while a value close to -1 indicates a strong negative correlation. A value close to 0 indicates a weak or no correlation. The calculated correlation coefficient between wins and revenue is approximately 0.999. This extremely high positive correlation suggests a strong link between on-track success and increased revenue. **Conclusion:** While other factors (economic recovery, new product launches) influenced Ferrari's 2010 revenue, this case strongly suggests a direct positive impact of improved F1 performance on market value. Conversely, teams with poor F1 performance often experience decreased market value. ### Case 2: 2017-2019 – Vettel's Era and Strategic Missteps This case examines the impact of Ferrari's performance from 2017 to 2019, a period marked by initial competitiveness followed by a decline. **Data:** | Year | Wins | Net Profit (thousand Euros) | |---|---|---| | 2017 | 5 | 537,396 | | 2018 | 6 | 786,627 | | 2019 | 3 | 698,708 | **Analysis:** Linear regression will be used to model the relationship between wins and net profit. Again, the full calculation is omitted for brevity, but the results are key. A simple linear regression would yield an equation of the form: `Net Profit = a + b * Wins`, where 'a' is the y-intercept and 'b' is the slope. The slope 'b' would indicate the change in net profit for each additional win. It's crucial to remember that correlation does not equal causation. While a strong correlation exists, other factors could influence Ferrari's profitability. **Conclusion:** 2017 and 2018 show a positive correlation between wins and net profit. However, 2019 demonstrates that even with a strong brand and competitive car, strategic errors can negatively impact financial performance despite maintaining a relatively high level of on-track competitiveness. ### Overall Conclusions and Limitations This analysis demonstrates a strong correlation between Scuderia Ferrari's F1 performance and the financial success of the parent company. However, it's crucial to acknowledge limitations: * **Data Availability:** Pre-1969 data is scarce due to Ferrari's private company status. * **Confounding Factors:** Other factors (economic conditions, marketing strategies, competitor actions, new product launches) influence Ferrari's market value. * **Causation vs. Correlation:** While a strong correlation exists, it doesn't definitively prove causation. Further research with more comprehensive data and a more sophisticated statistical model could provide a more nuanced understanding of the complex relationship between F1 success and Ferrari's overall market value.