Green Flag!
Which drivers are in the Top 5 for wins in the NASCAR Cup Series?
First, calculate total wins for each driver. Then, organize the drivers in descending order by wins, subset to keep the Top 5 winningest drivers, and feed the data into a horizontal bar chart (some other tweaks will be applied to enhance the visual output).
cup_series |>
group_by(Driver) |>
summarize(career_wins = sum(Win, na.rm = TRUE)) |>
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_series |>
group_by(Driver) |>
summarize(
career_wins = sum(Win, na.rm = TRUE),
total_races = n(),
win_pct = career_wins / total_races
) |>
arrange(desc(career_wins)) |>
slice_head(n = 5) |>
ggplot(aes(Driver, win_pct)) +
geom_bar(stat = "identity") +
coord_flip()
Accounting for total races run, we see a different story emerge. David Pearson’s win percentage leads the pack at 18.3%, followed by Richard Petty at 16.9%. Imagine how many more wins Pearson would have accumulated if he had competed in as many races as The King.
Modern driver performance: Analyzing race results
Let’s shift gears and look at how modern drivers stack up. The
Win column in our datasets makes it easy to filter for race
victories and analyze performance trends.
# Get all wins for a specific driver
bell_wins <- cup_series |>
filter(Driver == "Christopher Bell", Win == 1) |>
arrange(desc(Season))
# How many Cup Series wins?
nrow(bell_wins)## [1] 13Christopher Bell has 13 Cup Series victories. Let’s compare his performance across different track types to see where he excels.
# Average finish by track surface
cup_series |>
filter(Driver == "Christopher Bell", Season >= 2020) |>
group_by(Surface) |>
summarize(
races = n(),
avg_finish = round(mean(Finish, na.rm = TRUE), 1),
wins = sum(Win, na.rm = TRUE),
laps_led = sum(Led, na.rm = TRUE)
) |>
arrange(avg_finish)## # A tibble: 4 × 5
## Surface races avg_finish wins laps_led
## <chr> <int> <dbl> <dbl> <int>
## 1 road 31 13.4 3 121
## 2 dirt 3 14 1 100
## 3 paved 180 14.7 9 2492
## 4 <NA> 2 16 0 4Road courses show a strong average finish, but let’s visualize performance trends over time to see the full picture.
# Visualize season-by-season performance
cup_series |>
filter(Driver == "Christopher Bell", Season >= 2020) |>
group_by(Season) |>
summarize(
avg_finish = mean(Finish, na.rm = TRUE),
wins = sum(Win, na.rm = TRUE)
) |>
ggplot(aes(Season, avg_finish)) +
geom_line(color = "#fb9f00", linewidth = 1.2) +
geom_point(aes(size = wins), color = "#fb9f00") +
scale_y_reverse() +
theme_minimal()## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
The trend shows consistent improvement, with better average finishes in recent seasons. Lower numbers are better, so a reversed y-axis makes this clearer.
