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1-5 segment

As explained in Statistics Slam Dunk

A 1-5 segment is the group of players selected with draft picks numbered 1 through 5.

The 1-5 segment groups NBA players selected with draft picks numbered 1 through 5. It is one of six first-round segments used to compare career performance and productivity. section 3.5

Comparisons of regular-season games, minutes per game, career win shares, and win shares per 48 minutes show clear separation between the 1-5 and 6-10 segments. Players in the 1-5 segment averaged about 18% more minutes per game than those selected 6-10 and roughly 33% more than players in the remaining segments. section 3.5 section 3.5.2

Comparing minutes by segment

A grouped summary calculates each segment’s mean, median, and share of total minutes, then displays the results with paired ggplot2 bar charts. The charts make the gap between the 1-5, 6-10, and later segments visible. section 3.5.2

sumMP <- sum(draft2$MP)
draft2 %>% 
 group_by(Pk2) %>%
 summarize(mean = mean(MP),
           median = median(MP),
           pct = sum(MP)/sumMP) -> tibble2

mp1 <- ggplot(tibble2, aes(x = Pk2, y = mean)) + 
 geom_bar(stat = "identity", width = .8, 
          fill = "deepskyblue", color = "deepskyblue4") + 
 labs(title = "Average Minutes Played per Game",
      subtitle = "First-Round Selections between 
      2000 and 2009 NBA Drafts", 
      x = "Segment", 
      y = "Average Minutes Played per Game",
      caption = "regular season games only") + 
 scale_x_discrete(limits = c("1-5", "6-10", "11-15", 
                             "16-20", "21-25", "26-30"),
                  labels = c("1-5", "6-10", "11-15", 
                             "16-20", "21-25", "26-30")) +
 geom_text(aes(label = trunc(mean), vjust = -0.3)) +
 geom_label(aes(label = trunc(pct*100), vjust = 1.2)) +
 ylim(0, 30) +
 theme(plot.title = element_text(face = "bold"))

mp2 <- ggplot(tibble2, aes(x = Pk2, y = median)) + 
 geom_bar(stat = "identity", width = .8, 
          fill = "deepskyblue3", color = "deepskyblue4") + 
 labs(title = "Median Minutes Played per Game",
      subtitle = "First-Round Selections between 
      2000 and 2009 NBA Drafts", 
      x = "Segment", 
      y = "Median Minutes Played per Game",
      caption = "regular season games only") + 
 scale_x_discrete(limits = c("1-5", "6-10", "11-15", 
                             "16-20", "21-25", "26-30"),
                  labels = c("1-5", "6-10", "11-15", 
                             "16-20", "21-25", "26-30")) +
 geom_text(aes(label = trunc(median), vjust = -0.3)) +
 geom_label(aes(label = trunc(pct*100), vjust = 1.2)) +
 ylim(0, 30) +
 theme(plot.title = element_text(face = "bold"))

mp1 + mp2 + plot_layout(ncol = 1)

Use the supplied R listing to reproduce the segment-level minutes analysis and its mean and median charts. section 3.5.2

How Statistics Slam Dunk covers it

How deep it goes
Defines the segment and examines means, medians, and separation across performance measures, while stopping short of causal proof.
Examples
Compares 1-5 players with five other segments using minutes per game, career win shares, games played, and win shares per 48 minutes.
What you can do after
Evaluate how top-five draft selection relates to later NBA performance using grouped summaries, charts, and cautious comparisons.
How it connects
Connects draft picks, first-round selection, win shares, minutes per game, segmentation analysis, and tanking.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Statistics Slam Dunk. The section links point to the parts of the book it draws on.