README.Rmd

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output:
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    variant: markdown_github
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```{r, echo = FALSE}
knitr::opts_chunk$set(
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options(width = 110)
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## Predicting March Madness

Kaggle's [March Madness prediction competition](https://www.kaggle.com/c/mens-machine-learning-competition-2018/) is an accessible introduction to machine learning.  If you happen to like college basketball, you'll like that in this competition you can't bust your bracket, since you make a prediction for every game.  Plus this year there's a big prize pool, and luck plays a big enough role that you can be a legit contender fairly easily.

In 2016, my simple process using tidyverse functions in R placed in the top 10%.  I refined it a bit for 2017 and finished in the top 25%.

I'm sharing my code and process here for others to use as a starting point.  My approach is similar to that of the 2014 winners, Gregory Matthews and Michael Lopez.  They published [a paper about the role that luck plays in this competition](https://arxiv.org/abs/1412.0248), putting their model in perspective.  A takeaway: take  my model, tweak it a bit to generate some distance from the field, and you are competitive to win!

## What's here

In the Kaggle competition, you estimate how likely it is that Team A beats Team B, for each of the 2,278 possible matchups in the tournament.  **[My guide](march_madness_how_to.md)** documents a set of scripts for each step of:

* Deciding on possible input parameters
* Scraping the input data with the `rvest` package
* Cleaning and joining data sources to get tidy, prediction-ready data
* Training and evaluating machine learning models on the data
* Making and submitting predictions


## Licensing/usage

This code is public, please reuse it.  It's under an [MIT license](License.md).  Please acknowledge its role in any write-up or discussion of work that relies on it.  And if you win a cash prize from Kaggle using this, congratulations!  I wouldn't turn down a thank-you gift ;)

## Thanks

Thanks to contributors **@MHenderson** and **@BillPetti**.

## Contact me

Let me know what you think, either on twitter @samfirke or compose a friendly e-mail to: <img src = "http://samfirke.com/wp-content/uploads/2016/07/email_address_whitespace_top.png" alt = "samuel.firke AT gmail" width = "210"/>