Football Draft Order

submitted by Kevin Soo

Using ggplot2 and gganimate to visualize simple random walks, used to determine draft order in my Fantasy Football league.

Create data

Random walks for 4 entities for a predetermined number of steps. Each step, I draw from a normal distribution with mean of 1, so that numbers are generally positive. The cumulative sum will be plotted, to show which entity gains the highest score during the walk.

# Load libraries
library(tidyverse)
library(gganimate)

# Generate data
n <- 20 # Number of iterations/steps

# Random walk for 4 players
df <- tibble(
    ite = 0:n,
    Player1 = c(0, rnorm(n, mean = 1)),
    Player2 = c(0, rnorm(n, mean = 1)),
    Player3 = c(0, rnorm(n, mean = 1)),
    Player4 = c(0, rnorm(n, mean = 1))) %>%
    gather(player, score, Player1:Player4) %>%
    arrange(player, ite) %>%
    group_by(player) %>%
    mutate(totalScore = cumsum(score),
           size = ifelse(ite == n, 6, 4)) # This is used to size the labels

Plot data

Plot uses labels to represent each entity, and becomes larger at the final step so the final order is clearly visible.

# Plot data
ggplot(df, aes(x = player, y = totalScore)) +
    geom_hline(yintercept = 0, linetype ="dashed") +
    geom_label(aes(label = player, fill = player, size = size)) +
    theme_minimal() +
    theme(legend.position = 'none') +
    scale_fill_brewer(palette = "Spectral") +
    labs(title = "Random walk race",
         subtitle = "Iteration: {closest_state} of 20",
         y = "Score total",
         x = "Entity",
         caption = "Random walk simulated for 20 iterations") +
    transition_states(ite, transition_length = 1, state_length = 1, wrap = FALSE) +
    shadow_wake(wake_length = .1, wrap = FALSE)