Author: ferrt041

Uncategorized

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

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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!

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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…

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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

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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.

A New Point of View on Pokémon : The Results

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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?

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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?

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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

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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:

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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

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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

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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)

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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%

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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.

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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.

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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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Classification Tree, E-Sports, K-Means Clustering, Logistic Regression, NBA2k, nintendo, Overwatch, Propensity Modeling, Regression Modeling, Super Mario, Tree Based Models

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

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This panel was held on: Saturday, August 3, 2019 at 3 PM – 4 PM
And here was the pitch:
Join the data science debate of the highest critically acclaimed video games vs the nostalgia of games we grew up. The data science team at Pancake Breakfast: A Stack Of Stats will be serving up supporting data and driving the discussion for both sides of the debate. Panelists will debate greatest video game of all time or overrated!
The Panelist were myself and Stephen (an indie game developer).  Obviously Steve had the advantage going into this debate but it was really fun and the audience was very engaged, probably one of our best Q&A sessions of all time.
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Video Game Recommendation Engine – This is how we do it

These are data science panels and we started off this panel with a video game recommendation engine.  I had Stephen fill out a survey prior to the panel and from his results I built a recommendation model, with the goal of selecting games he has not played (he’s played a lot of games, so not an easy task) and would rate above average.

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How are we going to build this recommendation?  Through Propensity scoring!

A propensity score is an estimated probability that a data point might have the predicted outcome.

  • One of our panelists completed a survey and had to rank video games they have played
  • Their responses were linked to our ancillary data (critics score, user score, and genres)
  • Our model shot out a score between 0 and 1. The closer to 1 the more likely this game would be enjoyed by the panelist.

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Video Game Recommendation Engine – The Output

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For this panelist, the survey told us this about their gaming preferences:

The value User Score more than the Critics Score.

Their preferred genre is Action Adventure.

Their preferred platform is the PS2.

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Video Game Debate: Overview

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On the screen will be a video game, with some profiling data.

Panelist will debate the impact, perceived and replay value of the featured game.

Crowd will decide who made the better argument.

This is the meat of the panel., on the screen is also the IGN review headline and rating, Stephen and myself would take turns and argue if it deserved it’s ranking.

Goldeneye 007

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Stephen went first and argued that Goldeneye does not deserve this high of rating and his key point was on the replay value.  I attempted to argue on to value it at time of release.  The crowd sided with Stephen.

Pokémon Gold & Silver

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I went first this round and argued for the rating, this was a very pro Pokémon crowd.  Stephen brought up good points on where he thinks the series should go and adding another region is not the answer.  The crowd sided with Me.

Ultimate Marvel vs. Capcom 3

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Stephen chose to argue for this game, I wanted to throw a curve-ball in this debate.  It would have been very obvious if we chose Marvel vs Capcom 2, too easy.  I argued that it wasn’t even the best in the series, and the best in the series is actually X-men vs Street fighter.

Halo Combat Evolved

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Stephen was on team Halo for this one, I love Halo as well, but the crowd did not.  That was a shock to us but maybe Halo doesn’t have replay value?  Or everyone is getting tired with the series.

Battle Dome: Overview

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Two games go in… only one comes out

Panelists will argue for a game, they cannot both argue for the same game

The crowd decides who had the best argument

This was fun and challenging section of our panel.  I won’t go into details on this section but I do want to try something out.  As test to see who is interacting with my page by reading the data stories, I have a special giveaway.

Here are the rules, you must have an Instagram account. You must be following my Instagram account: @pancake_analytics.

To enter you need read through the battle dome section, screen shot your favorite match-up and post it to instagram.

In this post I want you tag @pancake_analytics and caption the post with “Who do you have in this Battle Dome match-up?”.

This giveaway will end on December 31st, 2019 and the winner will receive a Game-stop Gift card from me.  For to use on your next video game purchase in the new year!

Here’s the disclaimer I have to post:

Per Instagram rules, we must mention this is in no way sponsored, administered, or associated with Instagram, Inc. By entering, entrants confirm they are 13+ years of age, release Instagram of responsibility, and agree to Instagram’s term of use. Good luck!!!!!

Here’s the battle dome match-ups:

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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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K-Means Clustering, Logistic Regression, nintendo, Propensity Modeling, Regression Modeling, Super Mario

TBCC 2019 Smash Brothers, Segmentation & Strategy: Panel Recap

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This Panel was held on:

Friday, August 2, 2019 at 7:30 PM – 8:30 PM

During the Tampa Bay Comic Convention 2019, held at the Tampa Convention Center.

The Panelists were:

Tom Ferrara (@pancake_analytics) , Kalyn Hundley (@kehundley08), Andy Polak (@polak_andy)

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I want to take a quick moment to discuss the panelists.  I love giving as many different point of views as possible to these data science panels.  Without this variety of point of views it’s more of a lecture and less of a discussion.  This mix of panelists gave the audience the data science view, the tech industry view and the biological sciences view.  Best part about this is Smash Brother brought us all together.

Changing the Tier Conversation

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One of the main objectives of this panel was getting a discussion going on tier selection in Smash and how do we base tier selection in data science, and how do we validate our findings through one of the best players in the game.

A k-means cluster uncovers trends within our Smash Brothers 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 pros and cons of each main selection.

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A brief overview of a k-means cluster:

  • Standardize your variables
  • Analyze your elbow curve
  • Validate your clusters

Treat each game release as new product launch or a change in the market.

You would re-score your data, to understand the current market and you’re able to migrate and understand how the meta-game has changed.

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We end up with five unique clusters:

Floaters:

This group is the slowest by run speed and lightest by weight.

Jack Of All Trades:

They are middle group on everything, there is no distinct trend.

Dashers:

Like the Jack of All Trades group but faster.

Air Tanks:

Fast in aerial attacks and the heaviest of the characters.

Speedsters:

This group is the fastest and the lightest.

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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.

So who should be your main?  In this segment I rely on industry knowledge as well (ZeRo’s tiers as dependent variable).   I’ll build propensity score with the following independent variables:

  • Change in air acceleration
  • Base air acceleration
  • Base speed in the air
  • Base Run Speed
  • Character Weight
  • Ultimate Smash Bros. Cluster
  • Wii-U Smash Bros. Cluster

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What makes these three stand above the crowd?

The are middle ground on weight, fast air accelerators.

What are the differences between the three?

Wario has a slow run speed.

Palutena is the lightest.

Yoshi is the middle ground of this group.

The Curious Case of Ganondorf

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Ganondorf has more in-common with Jiggly Puff than he does Bowser.

The reason being is he’s quicker and can adapt well in aerial attacks and in falling than Bowser can.

On the flip-side of this I can also say Bowser more accurately represents how he’s viewed from the super Mario franchise, in Super Smash Bros. Ultimate.

Game Time: Name that segment: Overview

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I personally feel one of the best ways to reinforce learning is through a game.  For this panel I decided to reinforce the k-means segmentation and wanted volunteers to guess the segment 3 characters on the screen fall into.

Here was the overview:

5 Volunteers

On the screen will be 3 characters

All 3 characters belong to the same segment

Volunteers will do their best to convince the panel of which segment the characters fall into:

  • Floaters
  • Jack of All Trades
  • Dashers
  • Air Tanks
  • Speedsters

For participating volunteers receive a fabulous prize.

For this particular game the prize was an amiibo of their choice that works with Smash Ultimate for the Nintendo Switch.

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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Marvel Comics, Propensity Modeling, Regression Modeling

TBCC 2019 Avengers, Algorithms, and Analytics: Panel Recap

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This Panel was held on:

Friday, August 2, 2019 at 9 PM – 10 PM

During the Tampa Bay Comic Convention 2019, held at the Tampa Convention Center.

The Panelists were:

Tom Ferrara (@pancake_analytics) , Kalyn Hundley (@kehundley08), Andy Polak (@polak_andy)

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I want to take a quick moment to discuss the panelists.  I love giving as many different point of views as possible to these data science panels.  Without this variety of point of views it’s more of a lecture and less of a discussion.  This mix of panelists gave the audience the data science view, the tech industry view and the biological sciences view.  Best part about this is the avengers brought us all together.

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When I pitched this panel the idea was what happens when a data scientist gets hold of the infinity gauntlet?  Pictured above is a visual representation of how I’m going to use each stone.

Use the Time Stone to predict the box office sales for the MCU and determine the top influencers for success.

Use the Power Stone to eliminate low hanging fruit.

Use the Soul Stone to uncover the underlying attributes of the marvel universe.

Use the Space Stone to transport the marvel universe to their closest match.

Use the Reality Stone to show you the marvel universe in a new light, perfectly balanced.

Use the Mind Stone to convince you this matching worked.

Time and Power Stones: What is influencing the MCU box office success?

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I waked through those in attendance the output of regression model I built to unlock the the key influences of the Marvel Cinematic Universe and their relation to box office sales.

Considered influencers:

  • Rotten Tomatoes Scores (Critic and Audience)
  • Movie Release
  • Time since last MCU release
  • Solo Movie Releases
  • Was Iron Man in the movie?

Two Key Influencers stand out:

Having Iron Man in an MCU Movie drives in $100.5MM

The further along in the series drives in at least $216.8MM.  Story Development matters here’s the statistical proof!

Soul and Space Stones: Refitting the Marvel Power Scale

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During this panel I walked the crowd through the output of a second machine learning algorithm, a propensity score.

Ingredients in the batter:

  • Marvel Contests of Champions (MCC) Power Index Levels
  • MCC Health
  • MCC Attack
  • Marvel Battle Royale (MBR) Twitter Poll:
  • TTL Votes per round, Avg TTL Votes

Flipping the pancakes:

Predict the likelihood twitter would vote for a character

Re-purposing this score to apply it to characters not in the MBR Twitter Poll

Reality and Mind Stones: Perfectly Balancing the Marvel Universe

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This approach goes beyond ranking by attack, or defense.  This approach takes all those attributes together as well as the fan opinion.

If you only look at attack… you get skewed results

If you only look at defense… you get skewed results

A little bit of good… a little bit of crazy…

Old Man Howard the Duck?

Doctor Octopus the Demi-God?

Marvel Rapid Fire: Marvel Analytics Comparisons

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This was one of my all time favorite segments out of all the comic cons I’ve had the pleasure of paneling at.  Quickly I would show the audience an analytics technique and show them the Marvel equivalent.  I think this technique is very effective in reinforcing our learning and opening up data science to a new audience.

Everything we just went through were machine learning techniques

Machine Learning is the Taskmaster of Data Science

Learns from past data, trains, and attempts to apply this training to new data

When something new is introduced it takes time to catch up

A/B Testing and Incremental ROI is the plot of Civil War

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A neural network is Ultron… learns from observational data & figures its own solution

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Dr. Strange ran a logistic regression to find out the odds-on Titan

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Into the Spider verse was the perfect implementation of a random forest

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Game Time: Marvel Team-Up: Overview

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One of the best ways to reinforce learning is through a game.  During this panel I wanted to reinforce the learning from the propensity score.

I asked for 5 volunteers.  On the screen were 3 marvel characters.  2 characters on screen were look-a-likes (statistically speaking).  Volunteers did their best to convince the panel of which two characters should “Team-Up” or in other words identify the 2 statistically closest characters.

For participating all volunteers received a hero-clix figure of their choice.

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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Uncategorized

TBCC 2019 The Pokemon Journey Panel

ferrt041

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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

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.

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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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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nintendo, Propensity Modeling, Super Mario

Recipe 014: Smash Brothers Main Selection

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In this recipe I’d like you to chow down on a Smash Brother analytical approach to selecting your main character.  The approach I’m going to introduce you puts an emphasis on what makes a character unique.

pancakes_smash

 

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Before I start diving into the Smash Brothers data, let’s discuss the k-means clustering approach.  A k-means helps paint a clear picture of our data, in this case specifically it will identify Smash Brothers Characters by their attributes to create picture for who your main should be.  Our characters will be assigned into segments

(tiers… everyone loves to put tiers around Smash Characters but they’re based solely on opinion and player preference)

based on trends in our data, and how closely a character is to the a group.

Take the above picture, without applying this approach we are in the top left quadrant, we only have a faint idea of who should be our main.  As we apply more segments and more trends in the data we’ll eventually end up in the bottom left quadrant.  A clear picture of who our main should be.

Now I keep mentioning trends in our data.  How do we find trends in data where attributes are on the surface completely skewed and non-normalized?  Take for instance a characters weight as a whole number will be larger than a characters acceleration rate in the air (aerial attacks).

We can achieve these trends by standardizing our variables, setting all variables to have a mean of zero.  In doing so this analysis focuses strictly on the trends in our data and we can have a pretty interesting discussion: i.e. Yoshi is more similar to Kirby, than he is to Pac-man.

 

Super Smash Bros Ultimate Mural

 

In preparation for this data story I came across the following article, on Business Insider: “These are the 11 best ‘Super Smash Bros. Ultimate’ characters, according to the world’s number-one ranked player”

Here’s an excerpt from the article:

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And here is ZeRo being named the best overall player:

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This triggered a thought in my head and I haven’t done this on the Pancakes Analytics page yet, but typically you would bring a k-means cluster in production and re-score your segments on an agreed upon cadence.  In this case I’ll treat the release of a new game as the cadence.

I’ll run a k-means clustering on the character attributes in Wii-U version and then a k-means clustering on the same character attributes but for the Switch version.

While going through this process I’ll only be including those characters who were in both games and where the data is clean: i.e. all characters have a weight and all characters have available acceleration data.  Sorry Inkling, you’re not in this segmentation.

001_clust

002_clust

Above are both segmentation cadences and characters will be split into these segment tiers:

  • Floaters (Far right circle)
  • Jack of all Trades (Smack in the middle)
  • Dashers (Faster than your Jack of all Trades segment but not fast enough to be elite in that attribute)
  • Air Tanks (The bottom left circle)
  • Speedsters  (Top left circle)

These aren’t ranked by what tier is the best, but we can make some assumptions.  The Jack of All Trades segment, most likely you won’t be winning matches often but you’ll be competitive.

Smash Brothers is a unique fighting game, so characters do have a weight to them.  Being light weight does have it’s advantages, but the learning curve of playing as a Speedster might be too high risk high reward for you.

The Floaters, if you select someone with a weight advantage in this group, you’ll likely to win your match but you have to master the move set (your smash move).

Air Tanks, is a no brainer I think for any skill set.  If you want to have a high likelihood of lasting till time runs out, be an Air Tank (this won’t guarantee a win, that really depends on your competition).

 

003_gandorf

I’m hoping visual this stood out to you the reader: Ganondorf made a large leap from the Air Tanks to the Floaters.  This doesn’t only speak to Ganondorf but it also tells you information about Bowser as well.

When I speak to this to clients and those wanting to learn about a particular data, this is how it translates:

Ganondorf has more in-common with Jiggly Puff than he does Bowser.   The reason being is he’s quicker and can adapt well in aerial attacks and in falling than Bowser can.

On the flip-side of this I can also say Bowser more accurately represents how he’s viewed from the super Mario franchise, in Super Smash Bros. Ultimate.

Neither one of these characters were “nerfed”, only re-calibrated so there’s a distinct difference between the two.

What do you do with this information?  If you’re main is a Floater, Ganondorf would be a good transitional character if you were looking to play as a character with more weight.  Or say you always play as an Air Tank, because you have the assumption anyone who has Kirby as a main shouldn’t be playing Smash Bros. then Ganondorf is a good transitional main for you when you eventually given in and select Kirby, “by accident”.

Image result for kirby smash

 

Below are the segments a brief overview of those characters within each segments:

004_floaters

This segment has high variability and you can see this from the oblong shape of the circle.  Ganondorf and Jiggly Puff are driving this shape, all though they are in the same segment and are more similar to each-other than are to other segments, they are the furthest apart within this segment.

Now hold up… wait a second.  Didn’t I just try to prove a point of how similar they are?  Yes, but in relation of whose more similar to Ganondorf: Jiggly Puff or Bowser.  But if I posed the question who is more similar to Ganondorf: Jiggly Puff or Kirby… that answer is Kirby.

This group on average are the slowest by run speed and lightest by weight… they Float.

 

005_jackofalltrades

This segment is the medium of everything.  There’s no uniquely distinct trend in their data.  Now playing as Pikachu vs Mega Man would have so game-play differences but statistically speaking you are starting with same underlying stats.

If you’re new the series this a good group to start with… they’re a Jack of All Trades.

 

 

006_dashers

The Dasher segment is very similar to the Jack of All Trades segment, only slightly faster.  Playing in this group you could potentially do more harm than good, if you’re selecting because you want to stay middle ground. You could… Dash yourself off the area.

 

007_airtanks

Air Tanks are fast in the aerial attacks… and the heaviest?  I’m anticipating this group will be re-calibrated by the next release.  In other words… Bowser has no business being as effective as he is in the air as he weighs, normally these two variable don’t correlate.  I guess all the time battling a plumber who can flip and jumps is finally paying off.

 

008_speedsters

This is your high risk high reward group.  Characters in this segment are the fastest and the lightest.  I personally am awful playing as Sonic, he’s too fast for playing level but a seasoned player could probably mop the floor with Sonic.

for_post

So who should be your main?  In this segment I rely on industry knowledge as well (ZeRo’s tiers as dependent variable).   I’ll build propensity score with the following independent variables:

  • Change in air acceleration
  • Base air acceleration
  • Base speed in the air
  • Base Run Speed
  • Character Weight
  • Ultimate Smash Bros. Cluster
  • Wii-U Smash Bros. Cluster

propb

The output will give me the likelihood ZeRo would rank the character as a top tier character.  The highest influencers on predictability were:

Change in air acceleration

Run speed

The lowest influencers were:

Base air acceleration

Ultimate Smash Bros. Cluster (this highlights the bias towards the Wii-U stats, influencing ZeRo’s rankings)

Drum roll please….

main1

main2

main3

You should have your main be one of the above three.  This is the data solution to selecting your main.

Really looking forward to the comments section on this one 🙂

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final_002

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