Welcome and whether you’re a long time follower or new to the page, I’m glad to have you here.  First off let me get some updates out of the way and a little bit of refresher of what this site is all about.

My Pokemon data science YouTube Channel is progressing along nicely, the intro almost complete and the format for each video is almost nailed down.

A little background on me:

I’m a manger of decision sciences department and I want to give back to the comic con community.  I’ve been attending comic cons since high school (I won’t carbon date myself here but let’s just say my favorite video game is Civ II).

I want to teach, share, engage and learn from the comic con family, as well as share my years of analytics experience with aspiring analysts or those just looking to improve and understand the data behind their fandom.

This post is a new flow and stray from what I typically do, the idea here is to end with a Pokemon team recommendation for those playing the shield version and those playing sword (personally I picked up sword, love it).

I’ll walk you through a machine learning approach to building an optimized Pokemon team, the focus here is getting through the main story (in a different post I’ll give recommendations for a competitive team).

Excluded from our selection will be legendary and mythical Pokemon, I exclude these because they are in a league of their own and it wouldn’t be informative if I told you to use those as a top tier.

I have the underlying data for the Pokemon in the Galar region:

Base attack, base defense, base HP, base special attack, base special defense, base speed, version exclusives, and location in the Galar region.

In this approach I will avoid recommending Pokemon which must be caught in the “wild” area, but the the “wild” area is utilized to level up your team.  I highly recommend you use the raiding system in the wild to earn candy rewards and level up your Pokemon team quickly.

The final team recommendations will take inconsideration the moves they can learn, the gym leaders you face, as well as the champion at the end of the main story.  I’ll exclude the post game (hilariously ridiculous post game by the way, as most of the game has this quirky charm to it, I love it).

If you’re curious and want to do your own research in the image above I’ll be performing a K-means clustering which falls under unsupervised machine learning.

A K-means clustering algorithm will group our Pokemon into clear tiers.  Without running this algorithm we’re in the top left corner, and we can barely see our Yamper.

He’s a good boy, so we want to see him clearly and the more groups we choose the clearer we can see where Yamper ranks among all the Galar region Pokemon.

The above elbow curve helps in deciding how many tiers we should select.  When choosing the amount of groups or clusters, you need take a few thing into consideration:

How many clusters can I reasonably speak to?

What will give me a clear distinction between my Pokemon?

How quickly can I validate the clusters? (Selecting 12 clusters, it would take a while for it pass the sniff test.)

This bubble chart is a good visual representation of our clusters and the Pokemon of the Galar region.  A K-means cluster puts the focus on trends in the data, so all of those base stats I mentioned before our standardized.

Standardizing our variables means the mean of each variable is set to zero.  This is helpful if ranges or your underlying data is very different.  As it pertains to Pokemon this makes sure I have a good mix and I’m not only bucketing based on attackers and HP.

The above bubble chart has a lot of overlap in our clusters and how reasonable is it to bring a stage 1 Pokemon in your final team.

So I’ve got some data clean up to do, I’m going to limit my data to final stage Pokemon and I reduce the number of clusters.  I like to keep it at 3, so I can talk about high, medium and low value.

Now this is a much easier to read bubble chart.  Let me give a quick reference of what this chart tells us.

Cinderace is statistically more like Dragapult then it is like Coalossal, although they’re both fire types.  I want to move you away from the typing discussion for now.

Here’s a high level view of our clusters and to keep on them instead of high, medium, and low I’ve given the cluster poke-ball teamed names.

Now for each base stat there is high/medium/low but this in comparison to the other clusters.

The ultra cluster at a glance:

61% of the Pokemon in the Galar region, they excel in HP and attack.  They are not a handicap in any other categories, these should be the bulk of your team and are good to compliment if you are dipping into the other clusters.

The quick cluster at a glance:

11% of the Pokemon in the Galar region, they excel in defense and special defense.  They are the weakest cluster in all other categories.  I assigned them a quick ball because most likely you are catching these Pokemon to fill your pokedex in the post game.

A quick reminder about these clusters, is the focus here is the main story not competitive scene, sorry trick room fans.

The premier cluster at a glance:

28% of the Pokemon in the Galar region, they excel in Special attack and Speed.  They struggle in defense and special defense.  There’s a lot of gems in this cluster, but you’ll need to level them up, have a proper move-set and do the damage quickly.  Don’t drag out the battle with one these Pokemon.

Now what you’re really here for, the team recommendation.

The outcome of this is to use the findings from the K-means cluster and recommend a team based on the game version you are playing (either sword or shield).  All Pokemon must be catch-able beyond the “wild”.  Finally a starter must be selected.

You’ll see two charts below: a sword version and shield version.  Both will have you rounding out your final team before the fifth gym.

Thank you for reading through and feel free to reach out to me on instagram (@pancake_analytics) if you’d like the code that produces this, the data set, interested in an upcoming comic con panel, or just want to learn more about your fandom through data science.

Before I share the entire Anime Festival Orlando (AFO) 2019 Panel I’d like to give some insight on the nerves I had going into this panel and how the audience helped me get into the groove.

This panel I opted to go solo on, normally I have guest panelists join me, so the nerves where at all time high.

Could I keep the entire room engaged for a data science panel?  Would the flow drastically change?

I was set up ready to go early, and had great discussions with those who sat in early, we discussed whether or not to get pick-up Let’s GO Pikachu/Eevee.  Even one of the attendees were referred to attend panel from their friends who attended my Tampa Comic Convention Panels!

This was a first and good gut check for me, that what I’m trying to accomplish with Pancake Analytics is a good thing and is going over well.

I can’t thank the community we’re building here together enough!

This panel was held on: Saturday, August 10, 2019 at 8:30 PM – 9:30 PM

In Orlando, Fl during AFO 2019.

Our journey begins…

The steps on our Pokémon Journey:

• New Point Of View on Pokémon
• Field Researchers & Learning from them
• Pokémon Team Recommendations

A New Point of View on Pokémon : Overview

A k-means cluster uncovers trends within our Pokémon data to understand the relational similarities and differences on key in game attributes.

The more clusters the clearer our picture becomes and the deeper we can understand the Pokémon throughout our journey.

A New Point of View on Pokémon : The Results

A Brief overview of the approach:

Standardize your variables (Set each variable to mean of zero)

Analyze your elbow curve (Look for when the line plot elbows)

Validate your clusters (Perform a uni-variate analysis on core kpis for each cluster)

3 Distinct Groups:

High – Highest in all categories except for base defense and hp

Medium – Highest on defense, middle ground in everything else

Low – Only high on hp

What does this tell us about the starters?

The output of the k-means clusters can be used in to help determine your approach from the very beginning.

Reading the pyramid:

Easy path: (Build you team around this Pokemon & steamroll grind the competition)

Greninja, Swampert, & Sceptile

Hard path: (Need to acquire complimentary Pokemon, you learn more about Pokemon this way)

Serperior, Meganium, Torterra, & Chesnaught

How do we implement this scoring?

I needed more data to implement this approach.

I reached out to my instagram followers with a survey, and volunteers we’re given:

5 Questions:

What’s your ideal team of 6 Pokémon?

What year did you start playing Pokémon?

Do you play Pokémon GO?

How many Pokémon games have you played?

Do you play the Pokémon TCG?

Implementing the scoring: Trust The Process

A propensity model is a statistical scorecard that is used to predict the behavior of your customer or prospect base. Propensity models are often used to identify those most likely to respond to an offer, or to focus retention activity on those most likely to churn.

I used this model to predict if a Pokemon would be selected in the survey and used these results to recommend Pokemon a survey participants didn’t select but would give them statistically the same results of playing.

This is the whole Pokémon journey coming to a full circle.

The Pokémon Professor has done their own research and builds a model.

The field research team assists the Pokémon Professor with gathering new data.

The Pokémon Professor uses the model to assist the field research team.

Here are results of my recommendation model:

Is Ash getting better with each season?

I’ve analyzed all of Ash’s teams throughout the anime (from Kanto through XYZ).  I want to answer the question… Is Ash getting better with each season?

First challenge was how do we define success and what data science methodology do we use?

One area I feel gets over looked in data science is the performance analytics realm, using univariate and multivariate statistical analysis.

Univariate and multivariate represent two approaches to statistical analysis. Univariate involves the analysis of a single variable while multivariate analysis examines two or more variables. Most multivariate analysis involves a dependent variable and multiple independent variables

How do we determine success?

Base stats seem like a good starting point.

But as you can see one Pokémon can throw off our data… cough…  cough … Greninja cough … cough

Here’s how we do it, use the Pokémon GO Approach

As much as I feel Pokémon GO has flaws which shouldn’t get a pass, their CP attribute holds the answer to standardizing and scaling Ash’s teams.

What is CP in Pokémon Go?

CP (combat power) is not related to how much damage a Pokémon deals when attacking gyms, but is a combination of attack, defense and stamina (HP)

Using this approach helps level the field for those teams where Ash was heavy in one attribute, or when he only had one strong Pokemon.

From beginning to end Ash increased his CP by 8%

His best rotation was in Sinnoh

• He evolved the most Pokémon compared to his other teams.
• He evolved 3 Pokémon all the way to their final evolution.
• 3 of his Pokémon fall into our High cluster.

His worst rotation was in Johto

• He evolved only one Pokémon (Notctowl he found).
• He attempted to build a similar team he had in Kanto.
• Only 1 of his Pokémon fall into our High cluster.

Game Time: Let’s GO! Wonder Trade: Overview

I personally feel one of the best ways to reinforce learning is through a game.  During all of my panels I like to play a game that reinforces a machine learning technique, in this case the propensity model.

Those who participated received a rare Pokemon TCG EX/GX individual card, a unified minds unopened TCG booster pack, and a gift certificate to Burger King ( a meal on me ).

Food is usually hard to come by at a convention, so I went back to my younger roots, and thought well I would have loved to get a free meal at a convention.

5 Volunteers

On the screen will be 3 of Ash’s Pokémon

2 Pokémon are look-a-likes (statistically speaking)

Volunteers will do their best to convince the me of which two Pokémon are look-a-likes and who should be wonder traded

For participating volunteers receive a fabulous prize