8.4 Fielding Position

• We already have the fits, just filter by midyear

beta_coefs_a <- beta_coefs |>
  filter(Midyear >= 1985, Midyear <= 1995)

beta_fielders <- beta_coefs_a |>
  filter(
    Position %in% c("1B", "2B", "3B", "SS", "C", "OF")
  ) |> 
  inner_join(People)

## Joining with `by = join_by(playerID)`

ggplot(beta_fielders, aes(Position, Peak_age)) + 
  geom_jitter(width = 0.2) + ylim(20, 40) +
  geom_label_repel(
    data = filter(beta_fielders, Peak_age > 37),
    aes(Position, Peak_age, label = nameLast)
  )

## Warning: Removed 15 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_label_repel()`).

• I don’t see a clear pattern… thoughts?