Publications by Analysis of AFL
Building Your Own Brownlow Model
As some of you may be aware, this is the best time of year. Not only is it finals time but its also Brownlow week. During my honours year my thesis was on trying to predict who would win that years Brownlow medal. I have been running the model ever since. So instead of just giving a list of my predictions for this years count, this post is that w...
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State Space AFL
Ever read a post and went damn! I really wonder how that would work for AFL . Well that was me a couple of weeks ago reading this fantastic post and as most people know a good post is a fantastic post when it is reproducible. library(tidyverse) ## -- Attaching packages --------------------------------------------------------------- tidyverse 1....
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Make Your Own AFL Graph
“The greatest value of a picture is when it forces us to notice what we never expected to see.” – J. W. Tukey (1977) To learn a new skill I think there needs to be 2 main drivers An interest in a topic easy to get to tangible wins to encourage deeper understanding Hopefully you have stumbled across this blog because you have an interest ...
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FitzRoy
I don’t know if anyone knows this, but I love sports. A key barrier to entry for the growth of the AFL analytics community is simply data access which prevents not only people having a go at writing, but it also prevents current media having reproducible content. Whereas in overseas sports where data access is easier the sports analytics commun...
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Visualisation of Fantasy Points
Recently I was on twitter and saw this tweet here. Being a Connor Blakely fan I thought to myself I wonder if those are actualy good odds and if I could visualise this. So what is the theme of the thread, well it seems as though Chris and SgtButane think that Connor is likely to get more than 110 fantasy points and that he has a better fantasy re...
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Maths in Sport Script
############################################################################ #BROWNLOW PREDICTION WITH FREE DATA!!!! ################ ### ##from fitzRoy figures # ptm <- proc.time() library(tidyverse) df<-fitzRoy::get_afltables_stats(start_date = "1897-01-01", end_date = Sys.Date()) names(df) # df<-afldata::afldata team_stats<-...
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Visualisation of Squiggle Tipsters
Something I thought would be interesting is trying to visualise how the different tipsters on squiggle rate match-ups. A simple way to do this would be to look at squiggle margins by tipster and visualise it on a plot. To hopefully encourage you to give it a go at home why not change “West Coast” to the team you support to see if different sq...
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makemeauseR
The best way to learn things in my opinion is through examples. What this part of the site aims to cover an introduction to statistics course one might take at university. But instead of boring examples going to use much more exciting examples relating to AFL. These posts are by no means finals and will always be continously updated as I learn ne...
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Data Viz and Manipulation P1
This is the start of the tutorial series, where we will cover visualising and manipulating data in R. There will be a series of mini checkpoints that should be used as a guide to check understanding. To check the most basic functionality of R (you can use it as a calculator) what does 9+3 equal? 9+3 ## [1] 12 Checkpoint 1: Were you able to get ...
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Data Viz and Manipulation P2
Using fitzRoy and the tidyverse library(fitzRoy) library(tidyverse) ## -- Attaching packages ----------------------- tidyverse 1.2.1 -- ## v ggplot2 2.2.1 v purrr 0.2.5 ## v tibble 1.4.2 v dplyr 0.7.6 ## v tidyr 0.8.1 v stringr 1.3.1 ## v readr 1.1.1 v forcats 0.3.0 ## -- Conflicts -------------------------- tidyverse_...
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