A Classification Tree for Lebron James's Shots

Authors

• Alex Hope @ehhope
• Jes Simkin @jessimk

Objective

• Create a decision tree model for Lebron James's shots. Use our model to find the three strongest predictors for determining whether Lebron makes or misses a shot.

Results

• Using a decistion tree model our findings suggest (1) Shot Distance, (2) Shot Clock and (3) Touch Time were the strongest predictors in dictating whether Lebron James would make a shot or not.

Dependencies

• R:

  • tidyverse v1.2.1

• Python:

  • argparse v1.1
  • pandas v0.20.3
  • matplotlib v3.0.1
  • numpy v1.15.4
  • scikit-learn v0.20.1

Usage

Without Docker +git a Without Make

1. Clone this repo.
2. Run these commands:

Rscript src/01_loading_wrangling.R "lebron james"

data/shot_logs_raw.csv data/tidy_data_lebron_james.csv

python src/02_EDA.py data/tidy_data_lebron_james.csv results/figs/EDA "lebron james"

python src/03_machine_learning.py data/tidy_data_lebron_james.csv data/accuracies_lebron_james.csv data/features_lebron_james.csv

python src/04_analysis_plots_script.py data/accuracies_lebron_james.csv data/features_lebron_james.csv results/figs/train-test-acc_lebron_james.png results/figs/best_features_lebron_james.png

Rscript -e "rmarkdown::render('docs/Report.Rmd')"

With Make

1. With Make installed (install guides can be found here), clone this repo.

2. Run:

   make all

With Docker

1. Clone this repo and navigate to the repo's root.

2. Run the analysis within a docker container with the following code in command line:

    docker run --rm -it -v PATH_ON_YOUR_COMPUTER:/home/swish jessimk/dsci-522-jes-alex bash -c 'cd /home/swish; make'
   

3. To clean the analysis, use the following code in command line:

    docker run --rm -it -v PATH_ON_YOUR_COMPUTER:/home/swish jessimk/dsci-522-jes-alex bash -c 'cd /home/swish; make clean'
   

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What you'll find in this repo:

Data

• 2014-2015 NBA Season Shot Log from Kaggle; NBA Shot Logs Dataset

• Other CSV's created with our scripts

Documents

• Proposal

• Report

Results

• Figures from our Analysis

Scripts

• 01_loading_wrangling.R, loads and wrangles data

• 02_EDA.py, creates exploratory data analysis plots

• 03_machine_learning.py, performs machine learning

• 04_analysis_plots_script.py, creates plots from machine learning findings

✨Future Dev Note: We plan to update our scripts so that they are flexible and robust enough to be able to run our analysis for any player in the data set. Stay tuned.✨

Code of Conduct

Dockerfile

Makefile

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