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Pokémon TCG Market Analysis


Hi everyone! It’s Tom from pancake analytics and it’s been awhile but I proud to bring you a fresh stack of of analysis. Today’s analysis is on the Pokémon Trading Card Market, the recent boom, it’s decline and how we use the historic sales data to evaluate sets and cards we love to collect moving forward.

The boom of October 2020 was actually hinted at in January 2019 by forbes.com in their article titled “Trading Cards Continue to Trounce the S&P 5– As alternative Investments”. A line from this particular article which I find eye opening is:

“If a decade ago you had put your money in vintage and modern trading cards instead of the stock market, your payoff would be more than twice as big.”

A few more eye opening statistics from this article, the difference between the PWCC Top 500 Index 10 Year ROI and the S&P 10 Year ROI is 94%. That’s where our twice as big takeaway comes.

What is the reason behind this? Well maybe people have a deeper emotional connection to Charizard than they do to Amazon shares.


I’ve sourced Pokemon graded cards data from a site I recommend everyone visit: Pokemonprice.com.

Using this data and trending it over time here’s what I’m able to conclude from this analysis:

Regardless of the “Boom”, vintage cards grow. Let’s analyze the base set 1st edition non holo sales to explain this statement.

The average monthly two-growth of this set is 78% (excluding the boom data) and this is on par with last year’s two-year growth (2019 vs 2017) of 80%.

Pokémon Cards have shown a steady growth over time inline with the rest of the trading card industry. Although the monthly increase has declined by 7%, this speaks more to purchasing power of the market. Not everyone can afford to purchase a thousand-dollar card.

How much did the boom affect the sales? Looking at the total sales over time the boom begins in April 2020 there is a 98% increase in the total sold value of this set. Previously there was an increase of 45% in 2017, and 22% in 2018. In other words it doubled the growth, in a short period of time, this was never going to be sustainable.



We can learn and project sales from our data. The approach I’ll use is a holt-winters time series model. This approach works particularly well with sales data and especially if there’s a seasonal aspect ( i.e. high sales volumes over Christmas ).

A holt-winters uses three factors, a trend, a typical value and a cyclical repeating pattern. A trend is a slope over time, think what is our monthly change in sales? The typical value is used to add a value to recent sales, in this case I’ll use the average sale. Finally for the cyclical repeating pattern, which is a drawn out way to say seasonality, I’ll use our monthly sales figures.

We have our forecast built and it works, some might say it’s super effective, statistically it’s excellent. Mean Average Percent Error (also known as MAPE) will be our definition of success for this model, which stands at 8% . Anything below 10% is considered an excellent forecast and this is out of sample.

What I mean when I say out of sample, is I used historic data to predict values I already know occurred. This is best practice when building a forecast, please train and test your models.

The forecast has an excellent MAPE even with the boom occurring. The orange line is our forecast, and the blue line is the actual sales. I’m not confident the boom will be sustainable. You can see our forecast predicts sales which more align with the yearly growth I’ve mentioned previously. ( See the images below for the forecast lines )



The boom won’t last, but the two-year growth continues, this is promising for long term investing in Pokémon trading cards.

I’ll show two scenarios, the first scenario being the most unlikely which is the “Boom” is the new norm.

If the “Boom” is sustainable, the average growth in sales per card year over year, 2021 vs 2020, is more than double, 2.25 times more.

This average growth is $251 usd, which is roughly the equivalent of two modern booster boxes at retail price.

Now for the most likely scenario, the “Boom” ends.

If the “Boom” ends in early 2021 the market will be down year over year by 38%. There’s a bright side, this would still be an 84% growth compared to two year ago (2021 vs 2019). The average two growth would increase by 6%, still outpacing the stock market ( as called out by forbes.com in 2019 ).


Our forecast can be used to evaluate individual sets, this is were the fun of collecting what you love meets data science.

To show this application I’ve evaluated 3 sets which I feel are different from each other (variants, anime characters, etc.):

Jungle 1st Edition Holos

Gym Heroes Unlimted

Neo Genesis 1st Edition

Using the model I built and applying it to the individual sets here’s my evaluation and reccomendation:

Jungle 1st edition holos will show a growth of around 2.6x, that’s an increase of roughly $12K for the entire set value, by the end of December 2021.

Gym Heroes Unlimited non holos will show a growth of around 1.5x, that’s an increase of roughly $1.3K for the entire set value, by the end of December 2021.

Neo Genesis 1st Edition non holos will show a growth of around 2.8x, that’s an increase of roughly $39K for the entire set value, by the end of December 2021.

First glance you would think to yourself well, let invest in the Neo Genesis 1st Edition cards and forget the rest. I wouldn’t agree with you 100% if that’s what you want to do, I agree with you 100% if the first game you played in the series was gold/silver. Reason being is you have an emotional attachment to the Pokémon in that game, you are progressing your career/personal life to be able to invest in trading cards, so there will be more individuals like you, the market is there.

I will say don’t collect something you are not a subject material expert on and don’t collect something that the market isn’t there.

When I see these three sets compared to each other, I actually prefer Gym Heroes as an investment. The growth is there, you can argue it’s more sustainable because it’s only a $1K increase, and I personally feel the emotional attachment to gym leaders goes beyond the main series of video games, it leaks into the anime, cosplay at comic cons, and even some of the mobile apps (think Pokémon masters). Also from an ROI standpoint, you can acquire a lot of great cards from this set right now for a very low investment.


Long term investments in Pokémon Makes Sense. Trading cards as whole have outperformed the stock market over the past 10 years. The growth began well before the “Boom”, you can see this begin in 2018, and stay through 2019. The “Boom” won’t last, it’s currently doubling the price of cards. Let’s assume the “Boom” ends early 2021, the two-year growth still increases.

If you have time to invest, and can wait for a 10 year return, I’d recommend you do.




K-Means Clustering, Pokemon Go

Recipe 015: Pokemon Gen 3 K-means Clustering

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Take Charge of your Destiny!

In this data story I’ll be showing you how a self guided machine learning algorithm can select the best Pokemon squad for the Hoenn region.

At the end of this data story you’ll have

six Pokemon to look out for in Pokemon GO

, as well as understand why the Bagon Community Day was the best to date!


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As seen in the generation 2 games, the generation 3 games brought a wave of changes, especially the data structure.

Listed below are what I feel to be some of the major changes which effect the data of Hoenn region Pokemon.

Main Features added from Generation 2:
A complete overhaul of the Pokemon data structure:
Individual personality value
Abilities and Nature
The IV system went from 0-15 to 0-31
Damage such as Poison, Burn and Leach Seed (passive damage) are resolved at the end of the turn instead of immediately)
135 new Pokemon introduced
103 new moves were introduced
Weather can now be found on the field and activate at the start of a battle
Double Battles

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I’d like to call out double battles, as one of the main ingredients in my Pokemon evaluation soup is : Experience Growth Rate.

Double battles allow for more and quicker experience.

In other words all Pokemon can gain more experience earlier on in the game.

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If you recall when I looked at the data of the Johto Pokemon, we introduced to the very strong bugs.

Now in the Hoenn region we are introduced to weaker bugs.

This was done to counteract the impact of Heracross and Shuckle.

Catch these bugs below for the pokedex completion but you’re not going to have them on your main team.

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So these weak bugs aside you do get one of (if not the most) powerful dragons: Salamence.  If you play Pokemon Go, you most likely took advantage of in my opinion the best Pokemon Go Community Day to date (Held on 4/13/2019).

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One of my favorite sayings and motto is “Stay away from the brand names.”  What does this mean and how does it apply to Pokemon?  It means don’t buy into popular opinion, let the facts and data support your choices.

What’s all you hear about on community days?  If you screamed “shinys” then yes… that’s all you hear about.  How many shinys did you catch?

What’s your highest CP shiny?  I’ll trade for shinys.  Don’t be distracted by the brand name of community day, go for more than shinys.  Play in area with several poke stops and has cover from weather.

During the Bagon community day you should have been catching every Bagon spawn

, not only clicking in to see if it’s shiny.  Salamence is the goal, you want to be the mother of dragons (yes, I’m hype for Game of Thrones).

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Sticking to the theme of “Stay Away from Brand Names”, applying a k-means clustering algorithm will look for trends in the data and give us a group of Elite Pokemon we should replay Pokemon Ruby and Sapphire with and keep an eye out for in Pokemon Go.

How do we get to the ideal Pokemon team?  Applying a self guided machine learning approach: K-means clustering.  Now you can’t jump ahead and run the algorithm against your data.  First step is standardize your data, because you want to give each of your attributes an equal weight. 

Take for instance:

I want a well balanced team, I don’t want a team elite on attack but weak on defense.

After the data standardize and I run the k-means algorithm, you can see the scatter plot above.  The top right and far right cluster is the segment I want to build my team out of.  All other segments, you can win with but you can 100% steam roll the competition.

Below I’ve included visual representation of the top attackers and defenders in each cluster.


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This is great, love info graphics… but what do we do this knowledge?  Well we can build a team.

Your team building begins from the very beginning.

I’ll cut to the chase… you should chose Torchic (sorry Swampert fans)

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Why Torchic? Well I’m concerned about team structure and most importantly a showdown with Slaking (Fighting moves are must).  Below you can see the full recommendation of what your final team should look like.  You should also target all of these in Pokemon GO.

 

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K-Means Clustering, Pokemon Go

Recipe: 001 Pokémon Go K-Means Clustering Segmentation

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Here’s a treat for all the Pokémon Go players out there.  You’ll find below a recipe for a cluster analysis intended to guide you in building the most cost effective team of Pokémon. The goal of this recipe is to segment Kanto (GEN 1) Pokémon which can be found in the wild, with an emphasis on return on investment (ROI), or in this case Candy Cost investment and Gym Battle return.  Hope you enjoy! – Tom

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Good Old Fashioned Pancake Recipe:

https://www.allrecipes.com/recipe/21014/good-old-fashioned-pancakes/

Cool Pokémon Pancake Art:

Support our theme for this week’s analytics:

https://www.pokemongo.com/en-us/

https://www.pokemon.com/us/

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