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This is a walkthrough to share my learning journey throughout the years, using Shadowverse as an example.

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Finalysis Walkthrough

Note: If the notebook is taking too long to render on github, head to https://nbviewer.org/github/finalys/Finalysis-Walkthrough/tree/main/.

Introduction & History

Hi, I'm Finalys. You may know me as the author of Finalysis which started in 2020 as a personal project to learn about python.

Check out the very first Finalysis I tweeted.

Finalysis started off as a project to summarize and derive insights from Shadowverse tournaments, mainly SVO and JCG. Now that I've sort of achieved what I wanted to learn, it is time to share my knowledge as I try to understand how GitHub works (previously I've been doing everything on Jupyter). I hope to work with others in the future on GitHub :)

The purpose is to share my process of how I process data from Shadowverse Tournaments, which should be suitable for beginners like me. I am pretty sure there are better/efficient ways to perform such tasks. I'm not from a STEM background so results is what matters to me more. I'm not too concerned with the most time-optimal method since the data that we're working with isn't too large (pretty sure I gave the wrong justification but oh wells).

As this is my very first public repo, I will try my best to pick up good habits and make it very digestable for fellow beginners. I hope to inspire more people to take the very first step, ideally in something that interest you like how Shadowverse did to me.

Again, I reiterate that I still consider myself a beginner so if there are any improvements/suggestions please feel free to let me know!

Contact me via Discord Finalys#7064 and do follow me on Twitter!!

Coverage

Calling the API, creating hashes

  • File: ShadowverseAPI.ipynb
  • Retrieving useful information for shadowverse-portal
  • Cleaning the information
  • Other utilities: Scraping image assets
  • Output: SVCardInfo.xlsx

JCG Analysis

  • Files: JCGAnalysis.ipynb, DeckClassify.py, SVExcelFormatter.py
  • Scraping from the JCG website
  • Cleaning and getting player information & decks
  • Identify the deck archetypes brought by players & descriptive statistics
  • Breakdown of each deck brought by players
  • Matchup analysis
  • Export file to Excel

SVO Analysis

  • Files: SVOAnalysis.ipynb, DeckClassify.py, SVExcelFormatter.py
  • Identify the deck archetypes brought by players & descriptive statistics
  • Breakdown of each deck brought by players

Changelog

V1.6.2

V1.6.1:

  • Update for DeckClassify.py for Academy of Ages. Introduced hybrid deck identifiers; if a deck fulfills multiple archetype identifiers, it will be classified as a hybrid of said archetypes instead of an overwrite.

V1.6:

  • Major rehaul on SVOAnalysis.ipynb, SVExcelFormatter.py. Introduced more reliance on battlefy data to pull customFields to reduce manual checks across players' decklist and information.
  • Notebook visuals for SVOAnalysis.ipynb are removed, to be updated in the future.
  • SVExcelFormatter.py is now a standalone python file rather than a chunk of code in both SVOAnalysis.ipynb and JCGAnalysis.ipynb.

V1.5.2:

V1.5.1:

  • Error fix for JCGAnalysis.ipynb for BYE situations.

V1.5:

V1.4:

  • Added SVOAnalysis.ipynb.
  • Updated DeckClassify.py for identification housekeeping.

V1.3.1:

V1.3:

  • Added image utitlies in ShadowverseAPI.ipynb
  • Added 2 new sections to JCGAnalysis.ipynb: Brackets Page, Matchup Analysis
  • Updated SVCardInfo.xlsx for Celestial Dragonblade Bat Usher nerf

V1.2:

  • Added 3 new sections to JCGAnalysis.ipynb: Deck Classification & Summarization, Deck Breakdown, Exporting the results
  • Added DeckClassify.py as part of Deck Classification & Summarization
  • Added more visuals to JCGAnalysis.ipynb, more gifs!
  • Updated SVCardInfo.xlsx for Celestial Dragonblade expansion

V1.1:

  • Added JCGAnalysis.ipynb

V1.0:

  • My first GitHub commit!

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This is a walkthrough to share my learning journey throughout the years, using Shadowverse as an example.

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