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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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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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.

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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).


 

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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:

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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.


 

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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.

 


 

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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.


 

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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.


 

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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.


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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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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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Marvel Comics, Propensity Modeling, Regression Modeling

Recipe 013: Marvel Comics Propensity Score

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How crazy would it be if I told you Howard the Duck and Old Man Logan are closer to each other in skill sets than they are to any other Marvel characters?  Or how about Thor and Dr. Octopus are lookalikes as well?  Let’s answer these questions together by wrangling some readily available data.


 

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If I’ve learned anything from my career in data science it’s this: 80% of the work is data gathering and etl work, and 20% is analysis.

Nothing holds truer to this statement than finding data of Marvel characters skills set, on a normalized scale.  In this data story I’ll be using data from Marvel Contests of Champions (power index levels, health and attack) and the Marvel Battle Royale (a twitter fan poll of greatest superheroes).

A few more variables I’ll need to calculate around the results of the Marvel Battle Royale Twitter Fan Poll:

Total votes per each round

Average Total votes

A flag for if they were higher than average total votes per marvel character

This flag I’ll use as my dependent variable and my independent variables will be the Marvel Contest of Champions statistics.

What will this do?  This will predict the likelihood a Marvel Character would receive higher than the average total votes in the Marvel Battle Royale.

Once this is calculated I’ll receive an output of coefficients which I can apply to the rest of the Marvel Characters whom weren’t in the Marvel Battle Royale to create a propensity score.


 

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Now let’s back track a little bit and see why I’m going with a propensity model as opposed to a grouping by opinion.  I.e. Let’s put all the top attackers in the same category.

The top 3 characters based on Attack are Rocket Raccoon, Spider-man (Symbiote), and Blade.

In the above histogram, if you look all the way to the far right you’ll notice they are the data points on their own little island.


 

 

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Well what if I just grouped everyone by Health?  This data visualization looks more promising but mostly likely there would overlap on the other attributes and you wouldn’t be able to implement this successfully.


 

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The power index by definition could be suitable but from the top 3 selected on power index I can tell this rating wasn’t an index in the vein of what I would typically use an index for (time-series forecasting) and it looks to be similar to the Pokemon Go Combat Point System, the ability to use their full potential.


 

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One use of a propensity score is to create similar groups, based on the likelihood of performing a behavior.

In this case Doctor Octopus and Thor (Ragnarok) statistically the same in the Marvel Contest of Champions skill set.  For those of you want to go down and interesting rabbit whole, you can find YouTube videos on why Doctor Octopus should be in a demi-god tier.

This propensity score approach literally put Doctor Octopus in the same tier as a demi-god!


 

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Medusa by power index alone would be close to Thanos but factoring all skill sets, she is statistically closer to Gwenpool, Cable, and Nightcrawler than she is to the Mad Titan.


 

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Now for the crazy but statistically significant section.  Howard the Duck (I’m hoping he gets a show on Disney+) and Old Man Logan are a propensity score match.

An example like this where many begin to argue in data science, when does subject material expertise come into play?  We can argue significance forever, on any topic, but we can agree on all Marvel Champions have a value if played correctly.


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DC Comics, K-Means Clustering, Logistic Regression, Propensity Modeling

Recipe 011: DC Super Hero Throw Down: Propensity Modeling

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I want you to remember, Clark…In all the years to come… in your most private moments… I want you to remember my hand at your throat… I want you to remember the one man who beat you.

Chilling quote isn’t it?  That was said by Batman to Superman during the The Dark Knight Returns, a comic book miniseries written and drawn by Frank Miller.

One of the greatest debates in comic book lore and a fun discussion to have is pitting up two superheroes against each other… Who wins and why?  The below data story will introduce a data science approach to answering this debate.  To have fun with it… I’ve thrown characters from the video game Injustice 2 into a Superhero Thrown Down Tournament.


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Before we dive into the tournament and the results of the throw down, I’d like to touch on the approach: Propensity modeling.

Propensity modeling has been around since 1983 and is a statistical approach to measuring uplift (think return on investment).  The goal is to measure the uplift of similar or matched groups.

The heart of this approach lies within two machine learning approaches (segmentation and probability.)

Why propensity modeling for this exercise?  I wanted to rank my superheroes for the bracket using statistics (i.e. Batman is not getting a number one seed.)

35 characters were segmented on strength, ability, defense and health.  For the propensity score I gathered ranking information from crowd sourced websites and surveys.  Using this I was able to give an intangible skill score.  The reasoning was I wanted the medium of comics to do the majority of the work for me.  Comics are stories and the narrative drives the inner core of a character.  The higher a character is on a fan sourced website I’m assuming they are written well and are timeless.

Next step was to take the mean of the intangible skill score and flag those characters above the average (this will be my dependent variable for my logistic regression to calculate a propensity score).

What was thrown into the propensity model?  The skill sets gathered from the Injustice game, the assumption here is a character of Superman’s skill set would be written much differently then say Catwoman.

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Now it’s time for our throw down.

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The top four characters by propensity score were:

Cyborg

Supergirl

Aquaman

Black Adam

To determine a winner in the throw-downs characters were put up against each other in 11 categories.


Round 1 Takeaways:

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Our number one seed Cyborg nearly lost to Atrocitus. The result was 6-2-5, that’s read as six wins, 2 ties and 5 losses.

There were no upsets in the first round of play.  A few characters did not win a single category in their match-ups:

Harley Quinn (vs. Captain Cold)

Green Arrow (vs. Batman)

Black Manta (vs. Black Canary)

These three characters were ill-equipped to take on their opponent, it is possible they would have advanced given a new opponent.

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Round 2 Takeaways:

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Cyborg (our number one seed) defeated Captain Cold by a larger difference (+3 winning categories) compared to the previous match-up against Atrocitus, but he scored one win less.

We begin to see upsets in Round 2:

Robin defeated Black Adam by 1 winning category.  Wonder Woman defeated Firestorm by 4 winning categories.  Batman defeated Supergirl by 3 wining categories.

On propensity scores these were upsets, but from comic book debate standpoint you could argue these, i.e. given enough time to prepare Batman could defeat Supergirl.

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Round 3 Takeaways:

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Cyborg falls to Superman, loss by 4 categories.  This was the biggest fight Superman was given in this tournament to date (in both previous rounds he had 9 winning categories).

The upsets keep coming in:

Robin sneaks in a win again by 1 winning category (over Brainiac). Wonder Woman defeats the top seed in her region of the bracket (Aquaman) by 4 winning categories.  Batman defeated Green Lantern by 3 winning categories.

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Final 4 Takeaways:

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Robin’s Cinderella story comes to an end at the hands of Superman (winning in 9 categories).  Robin did fair better than those previously who gave Superman 9 category wins… Robin won in 2 categories.

Batman was able to upset Wonder Woman, by 2 winning categories.  We’re set for a championship round, the original who wins… Batman Versus Superman!

batman-vs-superman-movie


Our winner is…

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Superman defeats Batman.  Superman did not win in a landslide.  Batman loss by two categories but he was able to win in 5 categories.  Previously the highest total win categories against Superman were 3 winning categories.


What did we learn from diving into the DC data?  Comic book writing and fan perception goes along way in determining who wins a thrown debate.  If we use propensity modeling we can have more even playing field and limit the amount of unfair battles.


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SupermanPancakesW


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