AFO 2019 Player One, Power Ups, & Probabilities: Panel Recap




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


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


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