A Race Winning Strategy

NASCAR is one of the top-tier racing sports in North America and competes against F1 and IndyCar for the top viewership spot. Approximately 3.22 million people watch a race on any given weekend throughout the season. The nascaR.data package is the result of wanting to share a passion for the sport and provide an option to the typical go-to packages when learning new data visualization tools.

nascaR.data is packed full of NASCAR results dating back to the first Daytona Beach race in 1949! Use this package to discover race trends across the NASCAR Cup Series, Xfinity Series, and Craftsman Truck Series. Answer fun questions like “which driver has accumulated the most wins overall?”, “which owner has the best top 10 percentage at Daytona?”, or see which manufacturer has dominated which series in a certain season. It’s all here, so let’s strap in to our race seats, fire up those engines, and let’s take some warm-up laps.

Warming up the tires

nascaR.data provides access to 21 different datasets (7 per series) and are broken down by overall race results and driver, owner, and manufacturer season & career records. Let’s check our gauges and see what’s under the hood:

library(nascaR.data)

Use ?nascaR.data::cup_race_data to view a list of variable descriptions. This package has been designed to swap cup for xfinity or truck to see the same data structure (variables) for the respective series. Would you rather inspect driver-specific results listed by season or their overall career? No problem… this is an easy pit stop: cup_driver_career or xfinity_owner_season or truck_mfg_overall.

Note

Manufacturer (mfg) results use the term “overall” instead of “career” as is used for driver and owner.

cup_mfg_overall vs cup_driver_career

Green Flag!

First, organize the drivers in descending order by win. Then, subset to keep the Top 5 winningest drivers. Lastly, feed the data into a horizontal bar chart (some other tweaks will be applied to enhance the visual output).

cup_driver_career |>
  arrange(desc(career_wins)) |>
  slice_head(n = 5) |>
  ggplot(aes(driver, career_wins)) +
  geom_bar(stat = 'identity') +
  coord_flip()

Wow! This doesn’t even look like a close race. Richard Petty clearly leads the field with 200 wins. However, let’s take a drive a little deeper into the turn and account for the number of races each driver competed in. What if we compare these same five drivers by win percentage?

cup_driver_career |>
  arrange(desc(career_wins)) |>
  slice_head(n = 5) |>
  ggplot(aes(driver, career_win_pct)) +
  geom_bar(stat = 'identity') +
  coord_flip()

Accounting for Richard Petty’s 1185 races to David Pearson’s 575 races, imagine how many more wins Pearson would have if he competed in as many races as The King.

The Garage Area

Let’s go behind the pits and see what the manufacturers are up to in the Truck Series.

truck_mfg_season |>
  ggplot(aes(season, mfg_season_win_pct, group = manufacturer, color = manufacturer)) +
  geom_line() +
  geom_point()

No clear trend emerges, though it appears that there may be a 5-year clustering of winning percentage. For example, the Dodges experienced success in the early 2000s, but started to fall off before exiting the series. And while Ford has seemingly had gradual improvement, you can clearly see the success of the Toyota camp since joining the Truck series in 2004.

Post-race

cup <- cup_race_data |>
  mutate(series = 'Cup') |>
  filter(finish == 1) |>
  select(season, race, finish, money, series)

xfinity <- xfinity_race_data |>
  mutate(series = 'Xfinity') |>
  filter(finish == 1) |>
  select(season, race, driver, money, series)

truck <- truck_race_data |>
  mutate(series = 'Truck') |>
  filter(finish == 1) |>
  select(season, race, driver, money, series)

bind_rows(cup, xfinity, truck) |>
  group_by(series, season) |>
  summarize(mean_money = mean(money, na.rm = TRUE)) |>
  ggplot(aes(season, mean_money, group = series, color = series)) +
  geom_point() +
  geom_line()

Race winnings in the Cup series experienced exponential growth beginning in the 1980s while Xfinity and Truck Series winnings have remained relatively the same since 2000.

The Backstretch

I hope this gives you a little taste of what is included in this package. There’s plenty of opportunity to further clean and reshape the data for data visualizations or model prepping. I’ll be adding more data throughout the season.

Toolbox

nascaR.data was built with R version 4.4.0 with the tidyverse (2.0.0), rUM (2.0.0), and ggtext (0.1.2) packages used to preprocess and summarize data.^1–5

References

R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2024. Available from: https://www.R-project.org/

Wickham H. Tidyverse: Easily install and load the tidyverse [Internet]. 2023. Available from: https://tidyverse.tidyverse.org

Wickham H, Averick M, Bryan J, et al. Welcome to the tidyverse. Journal of Open Source Software 2019;4(43):1686.

Balise R, Odom G, Grealis K, Cardozo F. rUM: R templates from the university of miami [Internet]. 2024. Available from: https://raymondbalise.github.io/rUM/

Wilke CO, Wiernik BM. Ggtext: Improved text rendering support for ggplot2 [Internet]. 2022. Available from: https://wilkelab.org/ggtext/