Overview

In “A Better Way to Evaluate NBA Defense”, Nate Silver posits an interesting new approach to determining player defensive scores. The DRAYMOND score focuses heavily on understanding opponent’s shooting, rather than other stats like steals/rebounds, as a means of getting a more realistic picture of the impact individual players have on the court.

https://github.com/awrubes/Assignment-1_Data-607/blob/main/draymond.csv

glimpse(nba)
## Rows: 3,009
## Columns: 4
## $ season      <dbl> 2017, 2014, 2015, 2014, 2015, 2016, 2017, 2018, 2015, 2016…
## $ player      <chr> "AJ Hammons", "AJ Price", "AJ Price", "Aaron Brooks", "Aar…
## $ possessions <dbl> 331.02580, 211.71560, 633.51860, 3257.93400, 3984.04400, 2…
## $ DRAYMOND    <dbl> -0.1766801, 5.9121720, -1.7909210, -0.9529003, -0.1861272,…
# Rename columns for clarity
colnames(nba) <- c("Season", "PlayerName", "Possessions", "DRAYMONDScore")

# Sort the data by DRAYMONDScore in descending order
nba_sorted <- nba %>%
  arrange(desc(DRAYMONDScore))

# Subset the top and bottom players
top_players <- nba_sorted %>%
  slice_head(n = 10)

bottom_players <- nba_sorted %>%
  slice_tail(n = 10)

# Combine the top and bottom players
top_low <- bind_rows(top_players, bottom_players)

Visualize

# Combined scatter plot
ggplot(top_low, aes(x = Possessions, y = DRAYMONDScore, color = ifelse(DRAYMONDScore > 0, "Top", "Bottom"))) +
  geom_point() +
  geom_smooth(method = 'lm', se = FALSE) +
  ggtitle("Top vs. Bottom Players: Possessions vs DRAYMOND Score") +
  xlab("Possessions") +
  ylab("DRAYMOND Score") +
  scale_color_manual(values = c("Top" = "blue", "Bottom" = "red"))
## `geom_smooth()` using formula = 'y ~ x'

Conclusions

Based on the visual analysis, there isn’t a strong correlation between possessions and the DRAYMOND score among the top and bottom players. This suggests that other factors may play a more significant role in determining a player’s defensive performance as measured by the DRAYMOND score.