README.md

In npeters1322/bRacketbusteRs: Work with Kaggle's 2021 March Mania Data

bRacketbusteRs

The goal of bRacketbusteRs is to provide basic functions to work with Kaggle’s 2021 March Mania competition data.

Installation

You can install the development version of bRacketbusteRs by running the following code in R:

devtools::install_github('npeters1322/bRacketbusteRs')

If you get errors related to the kaggler package, try installing that package first by following this link and using the code in the “Installation” section.

Using the Kaggle Data

To use this package, users must be authenticated to use the Kaggle API and must have accepted the terms of the competition. To set that up, please follow these steps:

1. Go to Kaggle.com and sign in or create an account.

2. Find the March Mania competition by going to this page. Go to the data section and accept the terms if you have not already.

3. Click on the icon of your profile picture in the top right and then click “Account.”

4. Scroll down to the “API” section and click “Create New API Token.” It should automatically download a file called “kaggle.json” for you, which contains your username and API key.

5. Save the “kaggle.json” file somewhere it will be easy to find later.

6. Copy the “kaggle.json” file to “\~/.kaggle/kaggle.json” or install the kaggler package and then follow the steps listed here.

7. After that, you should be all set to use the package!

Examples of Using the Package

To download the data and read in any specific files of interest, try this:

#download and read in data
getNCAAMData()

The data will be downloaded to your current working directory, so making a project specifically for this is the best idea. Note that it may take a little while to download all of the data. When the popup appears telling you to choose files to read in, you can click cancel if you don’t want to read any in right away; the data will still be downloaded for you to read in later, either on your own or through the above function again.

Then, explore the other functions, such as one that will calculate per-game statistics for each team in each season. DIfferent data will be used depending on whether you specify stage 1 or stage 2 data from the competition. For example, you can calculate the mean, standard deviation, or both, depending on your input, for several statistics. Here’s an example of using both:

#using stage 1 data, calculate the mean and sd of several statistics
perGameStats(stage = 1, mean, sd)
#> # A tibble: 6,187 x 58
#>    Season TeamID meanScore sdScore meanOppScore sdOppScore meanFGM sdFGM meanFGA
#>     <int>  <int>     <dbl>   <dbl>        <dbl>      <dbl>   <dbl> <dbl>   <dbl>
#>  1   2003   1102      57.2   13.9          57        12.2     19.1  4.40    39.8
#>  2   2003   1103      78.8   15.3          78.1      12.8     27.1  5.07    55.9
#>  3   2003   1104      69.3   11.4          65         8.65    24.0  4.98    57.2
#>  4   2003   1105      71.8   13.1          76.7      14.0     24.4  4.73    61.6
#>  5   2003   1106      63.6   11.4          63.8      12.6     23.4  4.43    55.3
#>  6   2003   1107      65.9   11.7          75.7      13.7     24.0  4.17    57.5
#>  7   2003   1108      69.1    9.85         73.1      12.5     24.9  3.36    58.7
#>  8   2003   1110      66.4    8.85         61.9       8.49    23.2  3.92    53.5
#>  9   2003   1111      83.3   13.3          81.7      15.5     28.6  4.77    64.5
#> 10   2003   1112      85.2   10.4          70.2       9.36    30.3  3.94    65.7
#> # ... with 6,177 more rows, and 49 more variables: sdFGA <dbl>, meanFGM3 <dbl>,
#> #   sdFGM3 <dbl>, meanFGA3 <dbl>, sdFGA3 <dbl>, meanFTM <dbl>, sdFTM <dbl>,
#> #   meanFTA <dbl>, sdFTA <dbl>, meanOR <dbl>, sdOR <dbl>, meanDR <dbl>,
#> #   sdDR <dbl>, meanAst <dbl>, sdAst <dbl>, meanTO <dbl>, sdTO <dbl>,
#> #   meanStl <dbl>, sdStl <dbl>, meanBlk <dbl>, sdBlk <dbl>, meanPF <dbl>,
#> #   sdPF <dbl>, meanOppFGM <dbl>, sdOppFGM <dbl>, meanOppFGA <dbl>,
#> #   sdOppFGA <dbl>, meanOppFGM3 <dbl>, sdOppFGM3 <dbl>, meanOppFGA3 <dbl>, ...

The function also calculates the opponent statistics, too, so you additionally have a lot of data about their defense.

There are many other similar functions that provide other variables, such as tournament seeds, records, ranking statistics, and conference tournament winners. Additionally, there’s a function that essentially combines all of the other functions and produces a dataframe that contains a large amount of variables that might be used to predict games or make the process a little easier.

Another function produces an NCAA bracket that can have game winners left out, true game winners included, or predicted game winners (by you) filled in. For example, you can fill in the 2015 NCAA bracket with the true winners by using this code:

ncaaBracket(season = 2015, filled = TRUE)

After running the function, you can click zoom in the “Plots” tab in RStudio to make it full screen and read it easier. You can also then choose to export it and print it if you want. In the future, this function will be made more dynamic, allowing you to put your competition submission in and having the function fill in the bracket based on that.

More functions will be made over time, and all will be updated for the 2022 competition. Even if none of the other functions are useful to you, I hope the function to download and read in the data is and can help you explore the data on your own.

Notes

• The developer is in no way affiliated with Kaggle or the creation of the kaggler package. Therefore, any changes to the API could cause breaks that cannot be fixed by the developer.

• If you like to, or would like to, participate in other Kaggle competitions, make sure to check out the kaggler package more to better integrate R and Kaggle.

npeters1322/bRacketbusteRs documentation built on Dec. 22, 2021, 3:14 a.m.