Abstract

Formula One has expanded from a predominantly European racing series into a global racing championship over the past several decades. This study examines how driver nationality diversity, geographic expansion, constructor success, and home-country advantage have changed throughout Formula One history. Historical race, driver, constructor, and circuit data from the Formula One World Championship (1950–2024) dataset were obtained from Kaggle and joined using R. The analysis focused on podium finishes and included descriptive statistics, data visualization, and a chi-square test of independence to evaluate the relationship between home-country status and podium performance. Results showed that the number of countries hosting Formula One races increased over time, while the diversity of driver nationalities appearing on the podium remained relatively consistent. British drivers accumulated the greatest number of podium finishes overall, although the leading nationalities varied across decades. The chi-square analysis found no statistically significant relationship between competing in a driver’s home country and achieving a podium finish. These findings suggest that although Formula One has become increasingly global in terms of race locations, competitive success has remained concentrated among a relatively small number of driver nationalities.

Brief Summary

This project explores how Formula One has evolved from 1950 through 2024 by examining trends in driver nationality, geographic expansion of the race calendar, constructor success, and home-country advantage. Using historical Formula One data, the analysis investigates whether increased globalization of the sport has been complemented by greater diversity among successful drivers. Interactive visualizations, descriptive statistics, and a chi-square test were used to summarize historical trends and evaluate the relationship between home-country status and podium performance. Therefore, I wanted to look into the question, how has nationality diversity among Formula One podium finishers evolved from 1950–2024, and how does this relate to the geographic expansion of the race calendar? I also look into the a the supporting questions, 1. Which driver nationalities have accumulated the most podium finishes? 2. How has the number of distinct podium nationalities changed by decade? 3. How has the geographic reach of the F1 calendar changed over time? 4. Does a driver’s home race appear to be associated with a higher probability of achieving a podium finish? 5. Which driver nationalities are most associated with successful F1 constructors?

The analysis found that Formula One has expanded substantially in terms of the number of countries hosting races, increasing from 13 host countries in the 1950s to 26 in the 2020s. However, the diversity of podium-finishing driver nationalities remained relatively stable across decades. British drivers recorded the greatest number of podium finishes overall, although the dominant nationalities shifted throughout Formula One history. The chi-square test found no statistically significant association between home-country status and podium finishes, suggesting that drivers do not experience a measurable competitive advantage when racing in their home country.

Introduction

This project looks into the sport of Formula 1, its drivers, their nationalities, and the global expansion of the sport. This sport began in 1950, and since then, drivers have represented and raced for many different nationalities. Additionally, the constructors for these racers have brought their own national identities to the table. Additionally, this is anything but a stagnant sport, it has expanded around the world, bringing its excitement and energy to various circuits across the globe. This makes it quite the interesting setting to look into the globalization and competitive representation of the sport. Specifically, the podium finishers have been researched in this project to identify patterns amongst the very best in the Formula 1 sport.

I was really interested in this topic as Formula 1’s geographic footprint has been increasing, and I have seen the races pop up in America, drawing all kinds of people to watch the races. I wanted to see if the global expansion of the sport was similar to the expansion of competitiveness in the sport, such as seeing if more nationalities found their way to the podium as the sport has globalized. Examining both trends provides a way to investigate whether the spread of the calendar corresponds with the globalization of competitive success.

The objectives of this project were to measure podium nationality diversity and see what countries might be the most present amongst the podiums from the past several decades. Additionally, this project aims to identify historically dominant nationalities in the sport of Formula One, and to examine changes across decades, as well as quantify geographic expansion. It compares geographic expansion with nationality diversity and also test the relationship between home-race status and podium outcomes. Lastly, the project examines driver nationality and constructor success.

Data

The data source for this project is Formula One World Championship data from 1950-2024 obtained from Kaggle compiled from Ergast Motor Racing Data API. There are five files in this dataset, races.csv, results.csv, drivers.csv, constructors.csv, circuits.csv. I used different variables from each of these files, such as driver ID in the results and files columns, and circuit ID in the circuits column. I used joins to get all of the data I needed from these files to make my Formula One data set. Some limitations to my data set are that nationality doesn’t take into account the finishers birthplace, training location, or cultural identity. Also, counting the host countries does not measure the intensity of geographic participation. Additionally, the number of races that take place and the structure of the races has changed over time. Finally, defining home-country participation depends on matching the different nationality and race country labels which are not all equal.

Methods

Data Preparation

I identified podium finishes by filtering the positionOrder variable to less than or equal to three in order to analyze podium nationality and constructor success, creating the podiums dataset. For example, I used filter(positionOrder <= 3).

Next, I joined the different tables using driverId, raceId, circuitId, and constructorId, creating my f1_dataset.

Feature Engineering

I created the variable decade = floor(year / 10) * 10 to group races into decades so that I could complete a historical comparison across the whole dataset.

I also created driver = paste(forename, surname) in order to provide readable driver names for searching and presentation.

I standardized nationality labels into country names so that I could compare driver_country and race_country information more accurately. For example: British → UK American → USA Dutch → Netherlands German → Germany

I additionally standardized the race country variables in a similar way. Finally, I created a home-race indicator using home_country = driver_country == race_country This allowed me to identify whether a driver was competing in their home country. The home_country variable was then used together with the podium variable in the race_participation dataset for the home-race statistical analysis.

Descriptive Statistics

My project heavily relies on descriptive statistics to summarize historical trends in Formula One. I created nationality podium totals using count(driver_nationality) to determine which nationalities have accumulated the most podium finishes.

Nationality diversity was calculated using n_distinct(driver_nationality) which measures the number of unique nationalities represented among podium finishers by decade. Geographic diversity was calculated similarly using n_distinct(race_country) to measure the number of distinct countries hosting Formula One races during each decade.

Finally, podium rates were calculated using mean(podium) * 100 which computes the percentage of driver-race observations that resulted in a podium finish. These descriptive statistics were used throughout the visualizations and summary tables presented in the Results section.

Statistical Methods

A chi-square test of independence was used as the primary inferential statistical method. The chi-square test evaluates whether there is an association between home-race status and podium outcome. The variables used in the analysis were home_advantage and podium, which indicate whether a driver was competing in their home country and whether the driver achieved a podium finish. I first constructed a contingency table (home_table) and then performed the statistical test using chisq.test(home_table). The analysis produced χ²(1) = 2.42 with a p-value of 0.120. Because the p-value was greater than 0.05, there was insufficient evidence to conclude that home-race status and podium outcome are statistically associated. Therefore, the analysis did not find statistically significant evidence of a home advantage in Formula One.

No regression, predictive, or machine-learning models were used. The primary objective of this project was to describe historical patterns in nationality diversity, geographic expansion, and competitive performance. Descriptive statistics and visualizations were supplemented by a chi-square test of independence for the home-race analysis.

Results

The analyses examined nationality diversity, geographic expansion over time, home advantage, and constructor-driver nationality relationships. The following figures and table summarize the major findings related to these topics. Each section below corresponds to a different research question.

Figure 1 shows the number of distinct driver nationalities represented among Formula One podium finishers by decade.
Figure 1. Nationality Diversity of F1 Podium Finishers Over Time

Figure 1. Nationality Diversity of F1 Podium Finishers Over Time

Figure 1 illustrates the number of distinct driver nationalities represented among Formula One finishers by decade. Nationality diversity varied throughout Formula One history, with the number of represented nationalities ranging from 11 to 16 during the studied time period. Although the diversity of the sport varied throughout the decades, the trend indicated that multiple countries have been consistently represented. Diversity peaked in the 1970s and 2010s, before declining in the most recent decade.

Figure 2 displays the total number of Formula One podium finishes by driver nationality across the entire study period.
Figure 2. Total podium finishes by driver nationality.

Figure 2. Total podium finishes by driver nationality.

British drivers have the largest total number of podium finishes, with German and French drivers following behind. On the lower end, there are several nationalities that have little to no podium finishes in their history driving Formula One. This indicates that podium success has been dominated by a smaller amount of countries.

Figure 3 compares the five most successful driver nationalities across decades.
Figure 3. Top five driver nationalities by decade.

Figure 3. Top five driver nationalities by decade.

British drivers had consistently high podium totals across most decades and remained one of the strongest-performing nationalities throughout the sport’s history. German drivers experienced substantial growth beginning in the 1980s and reached their highest podium totals during the 2000s and 2010s before declining in the 2020s. French drivers were most successful during the 1980s but contributed fewer podium finishes in later decades. Finnish drivers became increasingly prominent beginning in the 1990s, while Brazilian drivers experienced their greatest success during the 1980s and 2000s.

Figure 4 illustrates the number of countries hosting Formula One races during each decade.
Figure 4. Number of Formula One host countries by decade.

Figure 4. Number of Formula One host countries by decade.

The number of host countries generally increased over time. Early decades featured races in relatively few countries, while the modern Formula One calendar includes substantially more host nations, demonstrating the sport’s continued international expansion.

Figure 5 compares the growth of Formula One’s race calendar with the diversity of driver nationalities achieving podium finishes.
Figure 5. Geographic expansion compared with podium nationality diversity.

Figure 5. Geographic expansion compared with podium nationality diversity.

The number of host countries increased steadily throughout the study period, rising from around 13 countries during the 1950s to approximately 26 countries during the 2020s. In contrast, the number of distinct podium nationalities fluctuated within a narrower range, remaining between roughly 11 and 16 nationalities across decades. Formula One expanded into substantially more countries over time, but podium nationality diversity did not increase at the same rate. This suggests that geographic expansion of the race calendar did not necessarily correspond to proportional increases in nationality diversity among podium finishers.

Figure 6 compares podium rates for drivers competing in home races and non-home races.
Figure 6. Podium rates for home and non-home races.

Figure 6. Podium rates for home and non-home races.

Drivers achieved podium finishes in 11.1% of home races compared with 12.8% of non-home races. This goes against popular expectation, as drivers competing at home did not achieve higher podium rates. A chi-square test of independence was conducted to examine the relationship between home-race status and podium finishes. The test was not statistically significant, χ²(1) = 2.42, p = 0.120, indicating insufficient evidence to conclude that competing in a driver’s home country is associated with a greater likelihood of earning a podium finish.

Figure 7 illustrates the relationship between successful Formula One constructors and the nationalities of drivers earning podium finishes for those teams.
Figure 7. Driver nationalities associated with Formula One constructors.

Figure 7. Driver nationalities associated with Formula One constructors.

Many constructors were strongly associated with particular driver nationalities. British drivers accounted for a large share of podium finishes among multiple top constructors, while German, Finnish, Brazilian, and Dutch drivers also appeared often for certain teams. The distribution suggests that successful constructors have historically relied on a relatively small group of highly successful driver nationalities.

Table 1 summarizes the number of distinct driver nationalities represented on Formula One podiums alongside the number of countries hosting Formula One races during each decade.

Table 1. Podium Nationality Diversity and Geographic Expansion by Decade.
decade distinct_nationalities distinct_host_countries
1950 13 13
1960 13 15
1970 16 17
1980 12 19
1990 12 19
2000 13 20
2010 16 25
2020 11 26

As shown in Table 1, the number of countries hosting Formula One races increased steadily over time, growing from 13 host countries during the 1950s to 26 host countries during the 2020s. In contrast, the number of distinct driver nationalities appearing on the podium remained relatively stable, ranging from 11 to 16 nationalities across decades. Formula One became increasingly global in terms of race location. This expansion did not correspond to a similar increase in the diversity of nationalities achieving podium finishes.

Table 2 summarizes the percentage of races in which drivers achieved a podium finish when competing in their home country compared with races held outside their home country.

Table 2. Home vs. Non-Home Podium Rates.
home_advantage podium_rate
FALSE 12.76
TRUE 11.13

Table 2 shows that drivers achieved podium finishes in approximately 11.1% of home races compared with 12.8% of non-home races. The difference is relatively small, but drivers were slightly less likely to finish on the podium when competing in their home country. Figure 6 provides a visual for this comparison, and the chi-square test showed us that the difference was not significant enough to provide meaningful evidence that there is a home advantage.

Discussion

F1’s geographic globalization and competitive nationality diversity are related concepts, but they do not necessarily develop at the same rate, and cannot always be directly compared or generalized. For example, the sport has expanded but this does not necessarily mean that the diversity of Formula One finishers has expanded much, or that these new circuits provide drivers with a home-advantage. The constructor relationships also seem to stay pretty rigid, and they rely on the same countries in many cases. However, we can clearly see that the sport has globalized and different many countries and individuals chances to compete, even if that didn’t necessarily mean that they earned a podium spot. These findings show how globalization of F1 can be measured, and they demonstrate the difference between geographic expansion and competitive representation. They provide historical context for understanding F1’s international presence and the interactive tables and map users to explore these trends themselves. The limitations to this study are that it looks specifically at podium finishers, so these findings cannot be generalized to the whole population of Formula One drivers. Additionally, the drivers nationality is not necessarily related to the country they are from or were raised in, it is the country they have chosen to represent, so this could potentially impact the home advantage variable. Also, the decades are not uniform in the number of races or the structure of the season, potentially impacting results. Lastly, there could have been home-race classification issues due to the differing country names in different variables.

Conclusion

Formula One has become increasingly global in terms of where races are held, but this geographic expansion has not been matched by a comparable increase in the diversity of nationalities represented on the podium. The number of host countries has steadily increased over the decades, however the number of distinct podium-finishing nationalities has remained relatively stable. British drivers maintain the greatest number of podium finishes overall, although the nationalities dominating the sport shifted across different decades. This demonstrates that success in Formula One has historically been concentrated among a relatively small number of countries despite the sport’s international growth. The analysis also found no statistically significant evidence of a home-country advantage. Although drivers competing in their home country had a slightly different podium rate than those racing abroad, the chi-square test indicated that this difference was not statistically significant. For the future, researchers could analyze all F1 driver participants, not just the podiums. This will go beyond just looking at the competitiveness of the sport, but one could generalize their findings. Also, the nationality diversity could be examined at individual race years in order to have a consistent structure. Birthplace vs. nationality could be compared to make sure one is looking at a true home-advantage. Future directions: Race-level analysis: Study trends year-by-year instead of by decade. Participation vs. podium success: Compare nationality among all participants against nationality among podium finishers. Geographic analysis: Examine continents, regions, race frequency, and circuit distribution.

References

Rao, R. (2024). Formula 1 World Championship (1950–2024) [Data set]. Kaggle. https://www.kaggle.com/datasets/rohanrao/formula-1-world-championship-1950-2024

Jolpica. (n.d.). Ergast Developer API. https://api.jolpi.ca/ergast/

R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/