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afl_prediction's Introduction

Bayesian Elo

For the implementation of Bayesian Elo as described in the blog post(s) , refer to src/bayesian_elo_parameter_estimation.R

You would need ProjectTemplate installed to easily load data sets and source functions. Once the package is installed run - library(ProjectTemplate) - load.project() to set up the environment.

Steps I followed during running the model(s) on a weekly basis during the 2018 season

Main data sources

Updating the model data weekly

  • Download the csv file from https://fixturedownload.com and copy it to the data folder. Filename looks like afl.2018.AUSEasternStandardTime
  • As of lately herald sun expert tips data is not available publicly so the following is not applicable.
  • Get the herald sun expert tipsters data (google for herald sun expert tips with the round number)
    • Edit the download_expert_tips_run_script.R in src folder and add the new link and edit the round and link_str.
    • Run the script and make sure that the expert_tips variable looks OK. The script would save the results to an RDS file.
  • Run clear.cache() and (re)run
    • library(ProjectTemplate)
    • load.project()

Run retro analysis for the previous rounds

  • Run src/reto_score_weighted_expert_tips.R
  • Run src/retro_score_simple_elo.R (make sure to update the round to the most recent completed round)
  • Run the ensemble models
    • logistic: Run src/retro_score_ensemble_logistic.R after editing the round
    • Bayesian: Run src/retro_score_ensemble_bayesian.R after editing the round

Plot graphs to compare different models for the current season

  • Run profiling/compare_models_historical_head_to_head.R

Run the predictions for the current round

  • Run src/season_2018_predictions.R after editing the round

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