Analysis Of AFL's Modelling Walkthrough
These tutorials will walk you through how to model the Brownlow Medal in R. We have teamed up with Analysis Of AFL, the co-creator of the fitzRoy package, to create an end-to-end walk through of how he modelled the Brownlow.
You are welcome to grab this code and chop and change the variables to how you see fit. Feel free to add additional variables and try to improve the model to make it your own.
The takinghomecharlie R script walks you through the modelling process. It is reproducible on any computer and uses the fitzRoy package to grab data.
The takinghomecharlie R Markdown Script has detailed explanations of each step - although you will need to render this yourself in R Studio.
Alternatively, we have rendered it for you, simply download the Taking Home Charlie HTML Document and read along on any browser. Note that unlike Jupyter Notebooks, this won't render on GitHub.
Betfair Data Scientist's Modelling Walkthrough
This tutorial will walk you through the EDA, feature creation and modelling process, and allow you to generate your own predictions for any year between 2012 and 2018. Below are the predicted top 15 for this year.
print(agg_predictions_2018.head(15)) player team predicted_votes_scaled match_id 0 T Mitchell Hawthorn 35.484614 20 1 M Gawn Melbourne 21.544278 22 2 D Martin Richmond 20.444488 19 3 B Grundy Collingwood 19.543511 22 4 C Oliver Melbourne 19.009628 20 5 J Macrae Western Bulldogs 18.931594 17 6 P Dangerfield Geelong 18.621242 21 7 D Beams Brisbane 17.621222 15 8 E Yeo West Coast 16.015638 20 9 L Neale Fremantle 15.495083 21 10 A Gaff West Coast 15.165629 18 11 D Heppell Essendon 15.083797 19 12 J Selwood Geelong 14.989096 18 13 S Sidebottom Collingwood 14.863136 18 14 N Fyfe Fremantle 14.692243 11
Note that there are a few library requirements to run the python notebook. These are:
Simply use pip install to install these libraries, or google how to install them. For h2o specifically, click the link and follow the instructions.