This tutorial analyses North Melbourne players as way of understanding data importation and visuals

What you’ll learn: - How to get team-specific data - Finding top performers

Install Packages (First Time Only)

install.packages("fitzRoy")
install.packages("tidyverse")

Load Packages

library(fitzRoy)    # For AFL data
library(tidyverse)  # For data analysis and graphs

What this does: - fitzRoy gives us AFL statistics - tidyverse for data manipulation and visualization

Step 1: Get North Melbourne Player Stats

# Download all 2024 player statistics
north_stats <- fetch_player_stats_afltables(season = 2024)

# Filter for North Melbourne AFL 
north_stats <- north_stats %>%
  filter(Playing.for == "North Melbourne")

head(north_stats)
## # A tibble: 6 × 81
##   Season Round Date       Local.start.time Venue   Attendance First.name Surname
##    <int> <chr> <date>                <int> <chr>        <int> <chr>      <chr>  
## 1   2024 2     2024-03-16             1635 Sydney…       8034 Callum     Colema…
## 2   2024 2     2024-03-16             1635 Sydney…       8034 Aidan      Corr   
## 3   2024 2     2024-03-16             1635 Sydney…       8034 Paul       Curtis 
## 4   2024 2     2024-03-16             1635 Sydney…       8034 Luke       Davies…
## 5   2024 2     2024-03-16             1635 Sydney…       8034 Kallan     Dawson 
## 6   2024 2     2024-03-16             1635 Sydney…       8034 Zane       Duursma
## # ℹ 73 more variables: ID <int>, Jumper.No. <chr>, Playing.for <chr>,
## #   Kicks <int>, Marks <int>, Handballs <int>, Disposals <int>, Goals <int>,
## #   Behinds <int>, Hit.Outs <int>, Tackles <int>, Rebounds <int>,
## #   Inside.50s <int>, Clearances <int>, Clangers <int>, Frees.For <int>,
## #   Frees.Against <int>, Brownlow.Votes <int>, Contested.Possessions <int>,
## #   Uncontested.Possessions <int>, Contested.Marks <int>,
## #   Marks.Inside.50 <int>, One.Percenters <int>, Bounces <int>, …

What this does: - Gets all North Melbourne player stats from 2024 season - Each row is one player’s performance in one game

Step 2: Find Leading Disposal Winner

# Calculate total disposals for each player
disposal_leaders <- north_stats %>%
  mutate(
    Player = paste(First.name, Surname),
    Disposals = Kicks + Handballs
  ) %>%
  group_by(Player) %>%
  summarise(Total_Disposals = sum(Disposals)) %>%
  arrange(desc(Total_Disposals)) %>%
  head(10)

# Display top 10
disposal_leaders
## # A tibble: 10 × 2
##    Player              Total_Disposals
##    <chr>                         <int>
##  1 Luke Davies-Uniacke             638
##  2 Harry Sheezel                   626
##  3 Bailey Scott                    467
##  4 Tom Powell                      462
##  5 Zac Fisher                      433
##  6 Tristan Xerri                   412
##  7 Darcy Tucker                    401
##  8 Colby McKercher                 381
##  9 Jy Simpkin                      378
## 10 George Wardlaw                  344

What this does: - Calculates total disposals (kicks + handballs) - Groups by player and sums their season total - Shows top 10 disposal winners

Graph 1: Top 10 Disposal Winners - North Melbourne

Type: BAR CHART

ggplot(disposal_leaders, aes(x = reorder(Player, Total_Disposals), y = Total_Disposals)) +
  geom_col(fill = "blue") +
  coord_flip() +
  labs(title = "Top 10 Disposal Winners - North Melbourne 2024",
       x = "",
       y = "Total Disposals") +
  theme_minimal()

What this graph shows: - BAR CHART of North Melbourne’s top ball winners - Shows who gets the most touches

Step 3: Find Leading Goal Scorer

# Calculate total goals for each player
goal_leaders <- north_stats %>%
  mutate(Player = paste(First.name, Surname)) %>%
  group_by(Player) %>%
  summarise(Total_Goals = sum(Goals)) %>%
  filter(Total_Goals > 0) %>%
  arrange(desc(Total_Goals)) %>%
  head(10)

# Display top 10
goal_leaders
## # A tibble: 10 × 2
##    Player              Total_Goals
##    <chr>                     <int>
##  1 Nick Larkey                  46
##  2 Paul Curtis                  30
##  3 Cameron Zurhaar              29
##  4 Eddie Ford                   15
##  5 Harry Sheezel                14
##  6 Jy Simpkin                   11
##  7 Luke Davies-Uniacke          11
##  8 Brynn Teakle                  9
##  9 Tom Powell                    9
## 10 Zane Duursma                  9

What this does: - Sums up total goals for each player - Removes players with zero goals - Shows top 10 goal scorers

Graph 2: Top 10 Goal Scorers - North Melbourne

Type: BAR CHART

ggplot(goal_leaders, aes(x = reorder(Player, Total_Goals), y = Total_Goals)) +
  geom_col(fill = "darkred") +
  coord_flip() +
  labs(title = "Top 10 Goal Scorers - North Melbourne 2024",
       x = "",
       y = "Total Goals") +
  theme_minimal()

What this graph shows: - BAR CHART of North Melbourne’s leading goal kickers - Shows offensive weapons

Step 4: Find Leading Kicker

# Calculate total kicks for each player
kick_leaders <- north_stats %>%
  mutate(Player = paste(First.name, Surname)) %>%
  group_by(Player) %>%
  summarise(Total_Kicks = sum(Kicks)) %>%
  arrange(desc(Total_Kicks)) %>%
  head(10)

# Display top 10
kick_leaders
## # A tibble: 10 × 2
##    Player              Total_Kicks
##    <chr>                     <int>
##  1 Harry Sheezel               330
##  2 Luke Davies-Uniacke         316
##  3 Bailey Scott                313
##  4 Zac Fisher                  306
##  5 Colby McKercher             255
##  6 Darcy Tucker                253
##  7 Tom Powell                  220
##  8 Jy Simpkin                  218
##  9 Luke McDonald               211
## 10 George Wardlaw              203

What this does: - Sums total kicks for each player - Shows who uses the ball by foot most

Graph 3: Top 10 Kickers - North Melbourne

Type: BAR CHART

ggplot(kick_leaders, aes(x = reorder(Player, Total_Kicks), y = Total_Kicks)) +
  geom_col(fill = "purple") +
  coord_flip() +
  labs(title = "Top 10 Kickers - North Melbourne 2024",
       x = "",
       y = "Total Kicks") +
  theme_minimal()

What this graph shows: - BAR CHART of players who kick the ball most - Important for team ball movement

Step 5: Find Leading Mark Taker

# Calculate total marks for each player
mark_leaders <- north_stats %>%
  mutate(Player = paste(First.name, Surname)) %>%
  group_by(Player) %>%
  summarise(Total_Marks = sum(Marks)) %>%
  arrange(desc(Total_Marks)) %>%
  head(10)

# Display top 10
mark_leaders
## # A tibble: 10 × 2
##    Player          Total_Marks
##    <chr>                 <int>
##  1 Harry Sheezel           133
##  2 Charlie Comben          122
##  3 Bailey Scott            115
##  4 Luke McDonald           105
##  5 Aidan Corr              103
##  6 Darcy Tucker            102
##  7 Nick Larkey              99
##  8 Zac Fisher               90
##  9 Cameron Zurhaar          89
## 10 Tom Powell               86

What this does: - Sums total marks (clean catches) - Shows best aerial players

Graph 4: Top 10 Mark Takers - North Melbourne

Type: BAR CHART

ggplot(mark_leaders, aes(x = reorder(Player, Total_Marks), y = Total_Marks)) +
  geom_col(fill = "darkgreen") +
  coord_flip() +
  labs(title = "Top 10 Mark Takers - North Melbourne 2024",
       x = "",
       y = "Total Marks") +
  theme_minimal()

What this graph shows: - BAR CHART of best catchers of the ball - Key forwards and defenders