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Machine Learning-based Performance Evaluation Web Platform for D3, Club and High School Sports Programs

This documentation is for a Windows system; adjust accordingly

Pass Analytics Machine Learning Code (Prediction Model):

Clone GitHub repo and open xG Model folder in development environment that supports Python (e.g. VSCode, PyCharm, IntelliJ, etc.)
Python Module Installation (via Command Prompt):

Numpy (scientific computing): pip install numpy
Pandas (data manipulation): pip install pandas
Scikit-Learn (ML model construction): pip install -U scikit-learn
Seaborn (data visualization): pip install seaborn
Matplotlib (data visualization): pip install matplotlib
Pygsheets (Google Sheets connectivity): pip install pygsheets
- My Google credentials will not be included in the repo, so follow this step-by-step guide for authorizing your own pygsheets: https://pygsheets.readthedocs.io/en/latest/authorization.html

Code Execution:

Read in desired test and training data sets via Google Spreadsheet or local .csv file.
- Download project training set events.csv from https://www.kaggle.com/gabrielmanfredi/expected-goals-player-analysis?select=events.csv
If updating existing Google Spreadsheet with prediction results: establish connection to specific spreadsheet via pygsheets.
If operating locally: include additional code to export output as .csv file for future use, or simply execute program without use of pygsheets.
Run dataMain.py to execute MoBot xG prediction model.
To create data visualizations: input spreadsheet data into dataViz.py , adjust to specifications, and execute.

Pass Analytics Web Development Code (Flask Web App):

Clone GitHub repo and open Flask App folder in development environment
Module/Library Installation:

Flask (Python web framework): pip install Flask
Pandas (if not already installed): pip install pandas
Pygsheets (if not already installed): pip install pygsheets
- My Google credentials will not be included in the repo, so follow this step-by-step guide for authorizing your own pygsheets: https://pygsheets.readthedocs.io/en/latest/authorization.html
Gunicorn (Python WSGI HTTP Server): pip install gunicorn

Running Web App Locally:

Alter HTML/CSS/JavaScript as desired (or leave it alone)
Type app.py on the command line and click Enter.

Example:

C:\Users\..\..\FlaskApp>app.py
 * Serving Flask app "app" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: on
 * Restarting with windowsapi reloader
 * Debugger is active!
 * Debugger PIN: xxx-xxx-xxx
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

Deploying Web App via Heroku (refer to links below):

https://www.geeksforgeeks.org/deploy-python-flask-app-on-heroku/
https://stackabuse.com/deploying-a-flask-application-to-heroku/
Note: other hosting options can be used, but this specific project makes use of Heroku.
- Heroku Alternatives: https://blog.back4app.com/heroku-alternatives/

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