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TBCC 2019 The Pokemon Journey Panel

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Welcome to the first recap of the Comic Con Data Science panels run by the crew at Pancake Analytics.  Before I dive into the recap of The Pokemon Journey panel held at the Tampa Bay Comic Convention 2019, I’d like to have a quick over view of why I’ve chosen this path.

One question I get asked often is where did I get the idea to apply the fundamentals of data science to comic, video games and all fanfare?

The answer is simple to me and is a core pillar of Pancake Analytics.  I want to teach, share, engage and learn from the comic con family.

I want to TEACH those who attend my panels or interact with this page an introduction to data science and how it can improve areas of your life you are passionate in.

I want to SHARE my years of analytics experience with aspiring analysts and those scared of statistics.

I want to ENGAGE with fans of comic, video games, anime, theme parks, all things geek! I’m one of you and love our conversations.

I want to LEARN your point of view of the topics I discuss.  How do we have a high level discussion about data that doesn’t feel like a math class?

If any these core pillars resonate with you, I hope you enjoy the content I produce and continue to join the discussions.


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The Pokemon Journey at TBCC2019 was held on Saturday, August 3, 2019 at 7:30 PM – 8:30 PM.
The pitch of the panel was as follows:
Going to Tampa Bay Comic Con⁉️

Join us in the lite heart-ed data science discussion of Pokémon. Journey from Kanto to the Alola region through machine learning. This panel is more helpful than a Pokédex.

The Panelist were myself and Steve (an indie game developer).  Here’s a commissioned piece I got from a comic con artist:
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Above is the a visual representation of the Pokemon Journey we are about to embark on.

The steps on our Pokémon Journey:

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

During the new point of view on Pokémon section, I walked through the audience of a K-means clustering algorithm to reset Pokémon tiers and move us away from only grouping Pokémon together by typing.

During the Field Researchers & Learning from them section, I walked through the audience how to utilize survey data to build recommendation engine ( companies as large as Amazon and Netflix use this technique).

During the Pokémon Team Recommendations section, I walked through the audience the output of the recommendation model and real life scenarios of recommended teams.


 

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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.

When you pick up a Pokémon game for the first time ever you are in the left square.  Running this algorithm will get you the bottom right sooner, a clear picture.


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A Brief overview of the approach:

Standardize your variables (bring your variables to a mean of zero)

Analyze your elbow curve

Validate your clusters

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


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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:

Greninja, Swampert, & Sceptile

Hard path:

Serperior, Meganium, Torterra, & Chesnaught


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How do we implement this scoring?

I needed more data to implement this approach.

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?


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This approach recommends a new squad of Pokémon to the field researcher!

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.

This 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 assist the Pokémon Professor with gathering new data.

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


Here’s the model at work, the input and recommendations:

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During the my data science panels I like to reinforce the learning through a game and participants get a prize from my own personal collection.  For this specific panel participants received an unopened pack of Team Up from the Pokemon TCG, and a Pokemon EX TCG individual card.

Here’s an overview of the game:

5 Volunteers

On the screen will be 3 Pokémon

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

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

For participating volunteers receive a fabulous prize


I want to personally thank everyone who attended the panel in Tampa, at the Tampa Comic Convention.  I look forward to meeting again in 2020.


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