The  Hermitcraft TCG Analysis is a several-month long project consisting of several spreadsheets and four write-ups, all regarding the progression of the meta of the trading card game featured on the Hermitcraft Youtube series. To create this analysis, I broke down each card into its individual stats and components: hit points attack strength, energy requirements, special abilities, and even type strengths and weaknesses. The current version of the spreadsheet tracks the values, rankings and usage of nearly 100 different cards, as well as 25 different decks featured in the series.

 

The full process of estimating a card's potential in the game is based on a series of calculations, starting with their type score. A card's type score is based on the average ranking of other cards within that type, as well as the ranking of types that they are strong/weak against. This is because cards of the same type use the same item cards for their attacks and will therefore likely be included in decks together. Synergizing with other strong cards, in turn, makes a card more viable. 

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A card's HP does factor in, but is first converted into a simplified value of the average hits that it will take to knock out a particular card, with the assumption that most attacks deal between 50 and 100 damage. The 50 HP range of 250-300 can be simplified into 6, 7, or 8 turns for a knockout, which is what matters much more than the specific value on the card. 

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Similarly, the attack and special attack values can be converted into the average uses required to knock out an opponent. This, along with the number and type of item cards required to use the attack, resulted in an overall attack score, based on the strength of the attack as well as its efficiency and ease of execution. The special attacks had an additional layer of complication, since some of them relied on coin flips or other factors to determine their overall damage output. For this, I used a simple probability system to calculate the average value of these special attacks, which I used in place of their given values. 

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I combined all of these values to create a theoretical ranking, intended to roughly determine the card's average performance in any given game. Once the game began to be played and players began to form decks, I also created a system to track the various decks in play and the cards contained in each of them. By tracking the matches, I could see which decks had higher win rates, and by extension, which cards were more likely featured in these decks. Although the current iteration is incomplete (I'm unfortunately a bit behind in recording the matches), a full version would be able to use these match logs to determine a card's average performance in the game itself. I plan to cross-reference this with my theoretical values to determine the accuracy of the model in the future as I continue the project.