Creating a simple Predictive Model using AFL Data
Marcus Rotili
2024-11-01
Predictive Modelling using AFL Data and Linear Regression
In this document we are going to explore how to create a simple
Linear regression model to predict the Supercoach scores of players. In
order to do this we will be utilizing the fitzroy package
and tidyverse package. The fitzroy package
will give us our data and the the tidyverse will give us
our data viz, and data manipulation tool.
For more info on the dplyr package click here
For more info on the fitzroy package click
here
The scoring system for supercoach will obviously influence the
relationships between the metrics.
Here
is a link with a basic rundown of the game.
Obtaining and Cleaning Data
Let’s install and load the required packages. I’ve copied the
install.packages commands so all you need to do is delete
the # and run the commands, then import the data like
so:
#install.packages("fitzRoy")
#install.packages("tidyverse") Delete the # if you need to install the packages
#install.packages("GGally")
#install.packages("rsample")
#install.packages("GGally")
library(fitzRoy)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(rsample)
library(performance)
afl_2024 <- fitzRoy::fetch_player_stats_footywire(season = 2024)## ℹ Getting match IDs✔ Getting match IDs [1.2s]
## ℹ Checking data on <https://github.com/jimmyday12/fitzRoy_data>No new matches found - returning data cached on github✔ Checking data on <https://github.com/jimmyday12/fitzRoy_data> [1.6s]EDA
Now that we have our data lets do a quick EDA, let’s have a look at the distribution of scores in the 2024 season.
ggplot(afl_2024, aes(x = SC)) +
geom_histogram(fill = "skyblue") +
labs(title = "Distribution of Supercoach Scores", x = "Supercoach Scores", y = "Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_bin()`).
Distribution looks good, there’s a slight tail but generally speaking the data won’t need to be transformed.
Determining which variables influence Super coach score the most
To do this part we will use the ggpairs function from
the GGally package.
Let’s select some simple metrics and plot their correlations to Supercoach Points:
str(afl_2024) #Viewing COlumn Names## tibble [9,936 × 42] (S3: tbl_df/tbl/data.frame)
## $ Date : Date[1:9936], format: "2024-03-07" "2024-03-07" ...
## $ Season : num [1:9936] 2024 2024 2024 2024 2024 ...
## $ Round : chr [1:9936] "Round 0" "Round 0" "Round 0" "Round 0" ...
## $ Venue : chr [1:9936] "SCG" "SCG" "SCG" "SCG" ...
## $ Player : chr [1:9936] "Isaac Heeney" "Nick Blakey" "Chad Warner" "Brodie Grundy" ...
## $ Team : chr [1:9936] "Sydney" "Sydney" "Sydney" "Sydney" ...
## $ Opposition : chr [1:9936] "Melbourne" "Melbourne" "Melbourne" "Melbourne" ...
## $ Status : chr [1:9936] "Home" "Home" "Home" "Home" ...
## $ Match_id : num [1:9936] 11174 11174 11174 11174 11174 ...
## $ GA : num [1:9936] 1 2 2 0 0 0 0 0 0 0 ...
## $ CP : num [1:9936] 18 6 11 15 14 10 6 9 7 1 ...
## $ UP : num [1:9936] 10 16 17 7 8 9 15 14 10 13 ...
## $ ED : num [1:9936] 18 18 19 10 12 13 14 11 15 18 ...
## $ DE : num [1:9936] 69.2 69.2 73.1 43.5 57.1 65 70 55 78.9 94.7 ...
## $ CM : num [1:9936] 0 1 0 2 0 0 0 0 0 0 ...
## $ MI5 : num [1:9936] 1 0 0 0 0 2 0 0 0 0 ...
## $ One.Percenters: num [1:9936] 4 6 2 4 1 3 0 2 4 1 ...
## $ BO : num [1:9936] 0 6 1 0 0 0 0 0 0 0 ...
## $ TOG : num [1:9936] 79 78 81 78 79 81 86 89 87 84 ...
## $ K : num [1:9936] 17 18 17 12 12 15 7 16 13 16 ...
## $ HB : num [1:9936] 9 8 9 11 9 5 13 4 6 3 ...
## $ D : num [1:9936] 26 26 26 23 21 20 20 20 19 19 ...
## $ M : num [1:9936] 5 9 4 4 0 4 6 2 6 8 ...
## $ G : num [1:9936] 1 0 1 0 0 1 1 0 0 0 ...
## $ B : num [1:9936] 1 0 1 0 2 1 0 1 0 0 ...
## $ T : num [1:9936] 7 0 1 6 7 3 4 2 0 3 ...
## $ HO : num [1:9936] 0 0 0 33 0 0 1 0 0 0 ...
## $ I50 : num [1:9936] 8 6 12 5 5 4 1 5 1 1 ...
## $ CL : num [1:9936] 13 0 2 9 6 3 1 2 0 0 ...
## $ CG : num [1:9936] 2 2 7 9 4 1 4 4 1 1 ...
## $ R50 : num [1:9936] 2 7 1 1 2 1 2 2 6 3 ...
## $ FF : num [1:9936] 2 2 2 3 2 1 0 1 1 1 ...
## $ FA : num [1:9936] 0 0 2 4 3 0 3 1 0 0 ...
## $ AF : num [1:9936] 121 99 88 118 77 87 79 69 70 91 ...
## $ SC : num [1:9936] 144 117 118 139 94 120 76 77 83 76 ...
## $ CCL : num [1:9936] 4 0 1 2 1 1 0 0 0 0 ...
## $ SCL : num [1:9936] 9 0 1 7 5 2 1 2 0 0 ...
## $ SI : num [1:9936] 12 4 8 7 4 9 3 4 2 1 ...
## $ MG : num [1:9936] 619 529 588 254 343 404 129 442 519 260 ...
## $ TO : num [1:9936] 2 3 7 6 7 4 3 8 3 2 ...
## $ ITC : num [1:9936] 4 6 2 5 5 2 2 3 7 3 ...
## $ T5 : num [1:9936] 1 0 0 0 2 2 0 0 0 0 ...ggpairs(afl_2024,
columns = c("SC", "GA", "CP", "ED", "CM", "K", "D", "M", "G", "B", "T", "HO", "CL", "FF", "FA"),
title = "Relationships with Supercoach Points in AFL Player Data") ## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
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## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).
This plot is a bit hard to see, let’s do half the variables at a time.
ggpairs(afl_2024,
columns = c("SC", "GA", "CP", "ED", "CM", "K", "D"),
title = "Relationships with Supercoach Points in AFL Player Data")## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).
Anything with a correlation that is >0.5 we will use to create the
equation for the Linear regression.
We can see here that Contested Possessions, Disposals, Effective
Disposals and Kicks all have high correlations.
Let’s look at the rest of the variables now:
ggpairs(afl_2024,
columns = c("SC", "M", "G", "B", "T", "HO", "CL", "FF", "FA"),
title = "Relationships with Supercoach Points in AFL Player Data") ## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 5 rows containing missing values## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_density()`).
From this we can see that the rest don’t have high corrrelations. So let’s get into building the model based on the variables we identified.
Building the Model
We will need to split the data into training and testing sets and build the model based on the training set.
afl_split <- initial_split(afl_2024)
train_lm <- training(afl_split)
test_lm <- testing(afl_split)
model_1 <- lm(SC ~ CP + ED + K + D, data = train_lm)
summary(model_1)##
## Call:
## lm(formula = SC ~ CP + ED + K + D, data = train_lm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -57.168 -10.182 -1.557 8.465 76.986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.81171 0.41665 35.55 <2e-16 ***
## CP 3.99673 0.07333 54.50 <2e-16 ***
## ED 3.67961 0.08735 42.12 <2e-16 ***
## K 2.70281 0.07281 37.12 <2e-16 ***
## D -2.08882 0.09775 -21.37 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.01 on 7442 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.7667, Adjusted R-squared: 0.7666
## F-statistic: 6116 on 4 and 7442 DF, p-value: < 2.2e-16So as we can see, the metrics we have chosen are statistically
significant.
Let’s explore an interaction affect between disposals and effective
disposals. This is signified by using *
model_2 <- lm(SC ~ CP + K+ ED * D, data = train_lm)
summary(model_2)##
## Call:
## lm(formula = SC ~ CP + K + ED * D, data = train_lm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.612 -9.942 -1.603 8.376 75.383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.329707 0.697548 11.94 <2e-16 ***
## CP 3.938480 0.072863 54.05 <2e-16 ***
## K 2.622261 0.072511 36.16 <2e-16 ***
## ED 4.368026 0.105173 41.53 <2e-16 ***
## D -1.594124 0.105967 -15.04 <2e-16 ***
## ED:D -0.036723 0.003185 -11.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.88 on 7441 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.7708, Adjusted R-squared: 0.7707
## F-statistic: 5006 on 5 and 7441 DF, p-value: < 2.2e-16Now let’s compare these models using the AIC value and RMSE.
compare_performance(model_1, model_2)## # Comparison of Model Performance Indices
##
## Name | Model | AIC (weights) | AICc (weights) | BIC (weights) | R2 | R2 (adj.) | RMSE | Sigma
## -----------------------------------------------------------------------------------------------------------
## model_1 | lm | 61484.5 (<.001) | 61484.6 (<.001) | 61526.0 (<.001) | 0.767 | 0.767 | 15.005 | 15.010
## model_2 | lm | 61354.7 (>.999) | 61354.7 (>.999) | 61403.1 (>.999) | 0.771 | 0.771 | 14.873 | 14.879We can see that model_2 has better performance on the
training data. Let’s see the performance on the testing data set.
test_predictions <- predict(model_2, newdata = test_lm) #Predicting the scores
test_results <- test_lm %>%
mutate(predicted_scores = test_predictions) #adding predicitions to the testng data set
#Let's plot the Actual vs Predicted Scores
ggplot(test_results, aes(x = SC, y = predicted_scores)) +
geom_jitter(width = 0.2, height = 0.2, color = "Black", alpha = 0.6) +
geom_smooth(method = "lm") + #Smooth Trendline
labs(
title = "Predicted vs. Actual Supercoach Scores",
x = "Actual Supercoach Score",
y = "Predicted Supercoach Score"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
We can see the predictions aren’t too bad. Let’s apply the model to the original data set, plot it again and then we can filter to a particular player and have a look at how it changes each game.
Applying it to the full data set
Let’s apply it to the full data set. It will be the same method as applying it to the testing set, however we just need to change particular naming conventions to suit the new dataset.
SC_predictions <- predict(model_2, newdata = afl_2024) #Predicting the scores on the full dataset
afl_2024 <- afl_2024 %>%
mutate(predicted_scores = round(SC_predictions, 0)) #adding predicitions to the full data set
#Plotting now
#Let's plot the Actual vs Predicted Scores
ggplot(afl_2024, aes(x = SC, y = predicted_scores)) +
geom_jitter(width = 0.2, height = 0.2, color = "Black", alpha = 0.6) +
geom_smooth(method = "lm") + #Trendline
labs(
title = "Predicted vs. Actual Supercoach Scores",
x = "Actual Supercoach Score",
y = "Predicted Supercoach Score"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
Looking at one particular Player
Say we want to look at one particular player, we can do this by filtering the data set to just the desired player. And let’s look at how predicted vs actual scores change over the season using a line plot.
Let’s use Isaac Heeney as an example considering he was a popular player in Supercoach last season.
isaac_heeney <- afl_2024 %>%
filter(Player == "Isaac Heeney") #FIltering to just Heeney
# Transform the data to long format for plotting
heeney_long <- isaac_heeney %>%
select(Round, predicted_scores, SC) %>% # Select relevant columns
pivot_longer(cols = c(predicted_scores, SC),
names_to = "score_type",
values_to = "score")
# Create the plots now
ggplot(heeney_long, aes(x = Round, y = score, color = score_type, group = score_type)) +
geom_line(size = 1) + # Lines connecting the scores
geom_point(size = 2) + # Dots on the lines
labs(title = "Predicted vs Actual Scores by Round",
x = "Round",
y = "Score") +
theme_minimal() +
scale_color_manual(values = c("predicted_scores" = "orange", "SC" = "black"),
name = "Score Type") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels for readability## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
So now we can see that the model does an okay job at predicting the scores. But the Round isn’t ordered properly. Let’s fix this:
# Converting the FInals series into rounds
heeney_long <- heeney_long %>%
mutate(Round = case_when(
Round == "Qualifying Final" ~ "Round 24",
Round == "Preliminary Final" ~ "Round 25",
Round == "Grand Final" ~ "Round 26",
TRUE ~ Round # Keep other rounds unchanged
))
# Now lets convert this column into numeric values
heeney_long <- heeney_long %>% # Remove "Round " from the Round column
mutate(Round = gsub("Round ", "", Round)) %>%
# Format Round to have leading zeros for single-digit numbers so it orders correctly in the plot
mutate(Round = sprintf("%02d", as.numeric(Round)))
# Create the plot again
ggplot(heeney_long, aes(x = Round, y = score, color = score_type, group = score_type)) +
geom_line(size = 1) + # Lines connecting the scores
geom_point(size = 2) + # Points on the lines
labs(title = "Predicted vs Actual Scores by Round",
x = "Round",
y = "Score") +
theme_minimal() +
scale_color_manual(values = c("predicted_scores" = "orange", "SC" = "black"),
name = "Score Type") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels for readability
Overall, the model effectively captures the trends, but the accuracy of the predictions could be improved. To enhance this model further, additional interaction effects and other relevant metrics could be incorporated.
Additionally, to take it a step further, simulations for the upcoming season could be conducted by creating matchups and using average metrics from last season. This approach is more advanced, but it will depend on the release of the fixture.
This document has provided an overview of the fundamentals for creating, applying, and visualizing the model. Feel free to use this resource to explore other players, teams, and analyses.
Happy coding!