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

Recipe 011: DC Super Hero Throw Down: Propensity Modeling

FerraraTom

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!

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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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Cosplay

Recipe: 007 Comic Con Cosplay and the Drivers of Instagram Engagement

FerraraTom


Halloween has recently passed and it’s a good transition into this week’s analysis;

Let’s face it dressing up on Halloween is the first step to cosplaying at your local comic con.

Cosplay can be a lucrative business if done correct, and many people do.  As you read through this week’s analysis I urge you respect and treat cosplayers as you would any other professional.  It that’s a lot of hard-work and dedication to master their craft as they have.


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A staple at any comic con is the Cosplay culture.  Fans show their appreciation and passion for beloved characters.  Cosplay can also be a lucrative business if you have a strong work ethic, are consistent, and dedicated to your craft.

Get out the hot glue gun and let’s start forming the foam!

I’ve gathered a random selection of Cosplay data from Instagram.  The cosplayers ranged from followers of +3 million to below 2K.  This alone posed an interesting challenge.  How do I normalize and standardize my data to fit into a model?

My solution was to factor in key performance indicators of Instagram success (regardless of being in the realm of cosplay) and implemented an engagement score for each cosplayer (like a customer value score).

To prevent confounding variables (influencers with a direct correlation to each other), I elected to excluded everything which went into the engagement score.

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My initial read shows this model is very predictive of the data sample gather from Instagram and the highest influencer with significance is the images of the Cosplayer where they are exposed (think NSFW but tasteful).  The amount of hashtags impact was skewed to a correlation of the more followers the less to no hashtags are used.


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If you’re a subscriber to this blog and enjoy the Stacks of Stats, you’ll recognize my preference for Q graphs.

There’s a large curl at both tails but most of the data fits well, so there won’t be a need to run a more complex model.

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What could be causing these extreme values towards the end of each tail?

While gathering and visualizing my data, I observed an interesting behavior:

The amount of hashtags deviates and almost has no correlation with engagement.

Driving the skew-ness is two factors:

Newer cosplay accounts use fewer hashtags at the beginning

Well established cosplay accounts use little to zero hashtags with their most recent posts.


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Our data story isn’t complete and once take the exposed variable to the profiling stage and begin to extrapolate the engagement impact, a telling data story begins to form.

For example, this table read as:

DC comics themed Cosplayers whom also happen to be exposed potentially drive nearly 700 more likes than cosplay images fully clothed.

In the case of what has the highest impact?  We can chalk up Nintendo to the champion and most of it is from the Bowsette trend. Potentially driving in a whopping +61K likes.

Interesting enough the runner up from a potential engagement impact standpoint is Scooby-Doo (Velma mostly), and the gap is less than 10K likes.

Does being exposed help all boost all themes of cosplay?  There is one theme in this sample where there was a negative relationship; Anime.  The possible reason behind this relationship is the niche fan base and attention to detail Anime fans have.  i.e. Hard to go as Sailor Moon without the bow.


 

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What have learned from diving into the Cosplay Data?

Being a top cosplayer on Instagram is as delicate as any social media fame.  Every post, every composition, every hashtag, every theme… can make or break your brand.  Not all cosplay needs to have a level of exposure to be successful, but it is a huge driver in engagement.

A few uses of this analysis are if you’re going to theme as Scooby-Do lean towards Velma and there’s enough out there for comparison.

If you’re looking for a large impact and a fan of video games, take dive at Bowsette (drives in a potential +61K likes).

Finally more hashtags does not mean more likes.

There’s more value in posting a cosplay of character you are passionate about and post relevant hashtags for more organic likes.

After you have consumed this meal, I hope you take these findings and improve your cosplay engagement.  Also as always enjoy the featured pancake recipe below!


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https://www.inquisitr.com/5035455/the-5-sexiest-female-cosplayers-to-follow-on-instagram/


 

 

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disney, Mickey Mouse, Regression Modeling, Theme Parks

Recipe: 006 Walt Disney World Parks and Resorts Revenue Influencer

FerraraTom

It all started with a mouse.  This mouse is turning 90 this year and Mickey Mouse has made his impact on society.  To celebrate, what better meal to cook us this week than Walt Disney World Data?  I’ll be challenging myself to

identify influencers on the Parks and Resorts Division’s yearly revenue.


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With Mickey Mouse turning 90 years old this year, what better meal to cook us this week than Walt Disney World Data?  I’ll be challenging myself to identify influencers on the Parks and Resorts Division’s yearly revenue.

My first approach was to identify what happens during the year the revenue occurs?

The number of Animated Movies released by Disney

The number of Animated Movies featuring Disney Princesses

The number of Attractions add at all four main theme parks and then parsing this information out by the individual park

The first run was not an effective model: most of the variability in the data was not accounted for, and there were no independent variables of significance.

So my next approach was how do I capture word of mouth on movies and attractions?  Secondly, how do I incorporate when Disney starts charging admission to children (currently 2 yrs and younger, enter the parks for free)?

To knock out two birds with one stone, I settled on let me test a rolling 3-year average of all behaviors.  The results were very favorable, 67% of the variability is explained and I have interesting independent variables of significance to make a telling data story


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If you’re a subscriber to this blog and enjoy the Stacks of Stats, you’ll recognize my preference for Q graphs.

There’s some curls at the tails but most of the data fits well, so there won’t be a need to run a more complex model.

Let’s take a bite into the initial read before accessing the financial impact of all these fun Disney variables.

I’ll caveat this, significance is in the eye of the beholder, and is up to interpretation of the  storyteller and data scientist.  The first read shows the 3-year average of total park attractions having the highest relationship to revenue and inversely the amount of attractions opened at EPCOT has significance but a negative impact on yearly revenue.

I’ll dive more into the individual impacts later, but I want to utilize my upper and lower bounds.


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The output of this model shows the impact in millions USD.  Analyzing the cone, this is where our fairy tale begins to take shape.

Potentially the average amount of attractions introduced at the all four major parks can drive in $1.6 million USD.

With the Magic Kingdom driving most of this impact:

New attractions added at the Magic Kingdom can drive in $4.5 million USD.

The average amount of the Disney Princess movies does have more of an impact than factoring Disney releasing an animated movie as the only criteria.  What’s intriguing is the variability of our upper and lower bounds, there is a possibility there could be a loss of $50.6M.

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What could be driving the inverse affect?  Multiple reasons:

1.The quality of the movie releases

2.The presence or in this case non-presence of a meet and greet at the theme park

3.The global economic climate (Less international travel impacts this!)


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What have learned from diving into the Walt Disney Data?

There’s a reason WDW is investing in new IP based rides at Epcot and Hollywood Studios: they’ve been launching the rides outdated with their audience and they drive the lowest impact currently on yearly revenue.  I anticipate Epcot to see a steady growth on impact when Guardians of the Galaxy and Ratatouille open and a few years have passed.

Finally a Princess Animated Movie drives in 1 million USD more than a regular animated move release.

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What could be the reasoning?  I’d guesstimate rides introduced at the Magic Kingdom (drives in +4.5M USD) is having a downstream affect on the Princess impact.  Most Princess interactions take place at the Magic Kingdom.

After you have consumed this meal, I hope you take these findings and with Mickey Mouse a Happy 90th Birthday. J  Also as always enjoy the featured pancake recipe below!


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https://disneyworld.disney.go.com/


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Regression Modeling

Recipe: 002 Marvel Cinematic Universe Regression Model

 


FerraraTomThere’s is no argument against the Marvel Cinematic Universe being a financial success.  I’ll try to identify variables which can equate to box office success. The goal is to fit a regression model to Box Office USD for Marvel Cinematic Movie releases.
*At the time of cooking Ant-man and the Wasp did not have finalized Box Office USD data (This movie was excluded.) – TF


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Thanks for stopping and chowing down on this Recipe (click the link for a reader’s friendly pdf version of this recipe)

Now try this delicious pancake recipe (with the Ironman Gold and Red finish) courtesy of Crème De La Crumb (Link Below):

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