Every NHL season there are 1,312 games played between the 32 teams. In each one of these games, there can be any number of penalties called. I want to provide people with the ability to make more informed statements when it comes to penalties that occur. The way that this program performs this is by parsing through the NHL website's game data and gathering all the penalties that occur. After gathering the information, the script hands the data to a SQLite Database.

Important Note: In the upcoming weeks, I intend on reviving this project. It has been out of date for at least 3 seasons (Last one was the 2020 season regular season). Everything below this point is related to the old version.

The goal of reviving this project is to upgrade it to latest version of Python, and to implement some better strategies that I've learned over the years.

There are really four main files of the project.

• PenaltyTracker.py - This is the main file. It will handle getting the list of games on a specific date, parsing through them for the penalty data, generating the HTML, uploading to parse, and uploading the file to my website.
• Penalty.py - This is the file that defines the Penalty objects used in the PenaltyTracker. I wanted to create my own data structure for this project and it made sense to separate it out into its own file.
• Tester.py - This file is where all the unit tests live. Right now it tests the PenaltyTracker.py and the Penalty.py files.
• DataChecker.py - It is used to quickly compare the data generated by the NHL's website and the data contained on my website.
• DatabaseManager.py - This file handles everything involving the Database. The reason I did this was to make it so I could change the database implementation completely without having to dig through all the other files.

• unittest module - One of the skill I wanted to strengthen was the ability to think through and use automated tests to insure that changes made to algorithms or functions didn't change the overall result.
• The SQLite3 Module - I use this to store data for the 2016-17 season.