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

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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nintendo, Regression Modeling, Super Mario

Recipe 010: Mario Kart Game-play Improvement Controller Trials

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Before I dive into this week’s data story, let me state why I love the Nintendo Switch.  I personally feel there’s a need for video games to be a social event, and couch co-op is a must have feature.  The Nintendo Switch offers several games which meet this need.

My family loves playing video games and most of all we love playing video games together.

Most of the Nintendo games I’ve grown up on and have played over the years, Mario Kart by far is one of my favorites.  I’ll admit my wife shows me how it’s done.


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What I do find interesting about the Nintendo Switch is the joy con controllers, there’s a learning curve (but a huge improvement on the Wii-mote) and most veteran gamers prefer an alternative.

One alternative is the wireless controller, very similar to the X-box controller format.  I did pick up the Yoshi version for my wife and she loves it and personally feels it improves her game-play.

I’d thought it was time to put this notion to the test, what impact if any does a wireless controller has on game-play performance versus using a joy con.

Mario Kart seemed like the logical choice for this is experiment, it’s a multiplayer game, you can standardize your users (via ride type and modifications), and performance is measured in a continuous variable of points.

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A total of 8 trails were ran under these conditions:

-Standard Kart

-Standard Wheels

-Standard Flyer

-Mushroom Cup

-50 cc length race

-2 gamers

Half through the trial one gamer switched to the wired controller (Test group) while the other gamer stayed on the single joy con (Control group).

Results were documented, and the etl. process began, points scored each race would be used as the key performance indicator.

I next ran a linear regression (great for evaluating an A/B test), with my dependent variable being the points scored after the event (introducing the wired controller) the two independent variables: Treatment and Pre Points Scored.

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In this model I wasn’t concerned with the r-squared value or the significance level of each variable.  The sample data was not large enough, this was closed circuit small market test.

The model itself did show to be significant, which is a good indicator I can continue with the results.  Evaluating my Q’s graph, I see the model fits well, the trend goes through all the data points.

In my summary fit I notice there is a positive relationship between treatment (group) and post points scores.  At first glance this says you improve your Mario Kart game-play performance if you play with a wireless controller.

To complete this story I want to know my upper confidence level to be able to know by how many points and is this enough to move me up the rankings.

Using a wired controller has the potential to increase a gamers point performance by over six points each race.

The average points differential between race placement is 1.2 points.  This 6-point increase is enough to move you roughly 4 places, depending on your historic placement.

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What have we learned from diving into the Mario Kart Data?

The controller you play with matters, switching to a traditional wired controller can potentially improve your point score by 6.5 points,

which depending on your average race placement can move you up 4 places in the final standings.

Observing the CPU controlled racers, Shy Guy performed the best with an average final placement of 2.8.  The heavy class overall was the weakest group but without Bowser, it could have been worse.  Bowser’s average final placement was 4th.

 

After you have consumed this meal, I hope you take these findings and enjoy your next Mario Kart Grand Prix.  Also as always enjoy the featured pancake recipe below!


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Classification Tree, Game of Thrones, Tree Based Models

Recipe: 009 Game of Thrones Survival of the Fittest

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“When you play the game of thrones, you win or you die.” — Cersei

Let’s bring this quote to life in what I like to call a survival tree of the fittest.  This week’s analysis will focus on the character survival in Game of thrones.  Chow down and enjoy!


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Winter is coming and you’d like to know your chances of survival in the Game of Thrones universe.

Let’s learn from those who have survived to this point and those who have met their unkindly fate.

To do this I’ll build a classification tree with my event being set to is the character alive (1 for yes, 2 for zero).  Classification trees in general test the null hypothesis, when we reach my tree visualization I’ll assign the color red to instances of were it’s highly probable of a character death.  Green leaves will indicate it’s highly probable a character survives… as long as all this criteria is met.

Think of this tree as a really morbid family tree, but since the data is Game of thrones it fits right into place.

The variables have readily available to me (hopefully they have importance) are as follows:

  • House Affiliation
  • Member of nobility
  • Marital Status
  • Gender
  • Family history of deaths
  • Popularity

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From the initial read I see knowing if a character is popular among fans and if they are male hold the highest importance in determining survival.

Also the variables I have available account for 75% of the variability (a 25% miss-classification rate).

Let’s say you moved to Westeros, out of the gate you have a 25.4% chance of meeting your end.  At those odds I’m taking my chances but I should stay under the radar as much as I can, because the data warrants it.

If you become a popular character or are an integral part of the story, your death becomes more meaningful and your probability of survival is worse than a coin flip.

So let’s say you’re a like-able character (you can’t help it), not all is loss, as long as you’re a female.  The highest survival rate is the popular female character group.  This is a classic tale of high risk high reward.

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A classification tree is a great way to visual your data and now I’ll walk us through this Game of Thrones survival tree.

Let’s start at the very top, the tree assumes everyone has a 75% of survival.  Now as the tree splits this Is where the interesting part begins, and our data story begins to unfold.

If you are a popular character you flow to the left side of the tree, your survival rate of 75% now drops to 48%.

Staying to the left side of the tree there is another important split, are you a male or female?  Female characters have a higher probability of surviving (87% if you’re popular and 76% if you’re under the radar).

If you’re a male and you’re popular you have a 42% chance of survival (We’re looking at your Peter Dinklage).

Now here’s the largest caveat to take with this classification tree: I’m assuming it will no longer be relevant after the final season.  Winter is coming and most likely our characters will see their end by hands of White Walkers.


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What have we learned from diving into the Game of Thrones Data?

Everyone has starts off at a 75% survival rate and as your popularity grows your survival rate lessons by 27%.  If you’re a male your survival drops again by 33%.  If you’re a popular female character you are 45% more likely to survive versus your male counterparts.

An interesting tidbit…If you become popular and you are a female (hopefully the mother of dragons) you boast the highest survival rate of anyone in this universe, 87%.

 

After you have consumed this meal, I hope you take these findings and enjoy your episode of Game of Thrones. J  Also as always enjoy the featured pancake recipe below!


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https://gameofthrones.fandom.com/wiki/Game_of_Thrones_Wiki


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Board Games, Logistic Regression, Regression Modeling

Recipe: 008 Likelihood a Board Game Is Universally Loved

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For this week’s analysis I’m taking a different approach to the introduction.  I reached out to @missionboardgame to write the forward.  They are a couple from Turkey who tries their best to inspire people to join board game community.  With out further ado here is there overview of the modern board gaming climate:

We think a successful modern board game should include the following features:

✔️Your decisions should have an impact on the game progress.
✔️Minimal randomness.
✔️No player elimination as possible as there can be throughout the game.

In addition to those, theme, artwork and mechanics are also significant for our decisions while purchasing board games. Therefore, our favorite game is Robinson Crusoe: Adventures on the Cursed Island. It is a cooperative survival game where you are trapped on a deserted island. Each decision you have made previously has an outcome afterwards. The harmony between the theme and the rules is perfectly arranged so that you feel very integrated to the game. By this way, every action you take seems meaningful and logical. Also we love feeling the cooperation among us since we are usually 2 players. – Mission Board Game

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Countless nights I’ve played board games among friends and family.  Every new year’s eve my family and I play Monopoly.  A few reasons: the game-play length, the amount of players, and the simplified game-play.  I have 5 siblings, so saying it’s difficult to find a game for all of us to play is an understatement.

The reasons why we enjoy board games is an interesting topic.  Is it the theme of the game?  Is it the amount of players required?  Has the game received universal praise from critics alike? Is it a common game most households own, and we grew up playing?

All the above-mentioned variables I’ll throw into a logistic regression model and use the Bayes theory of probabilities, to determine the probability of a board game player will rank a game higher than the average score.

During the first read I see the model is statistically significant based on a z score of less than .05.   A few things stand out to me immediately:

1.) Not all variables have a positive relationship to a highly scored board game

2.) There are some strong social elements going on here (i.e. the longer the play the higher the impact may imply games which encourage discussion are rated higher)

3.) Fantasy themed board games are not ranked high (I have a D&D and video games impact theory)

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Before jumping into the positive relationships, I’d like to touch briefly on the negative relationship independent variables.

1.) Fantasy Theme: I included this variable in the model expecting to see a very high positive correlation, but I was very wrong.  To quote Rick and Morty : “Sometimes science is more art than science.”  In the spirit of the quote, I’ll assume there are threats to the fantasy themed board games genre, in the form of Role-Playing Video Games.  The storytelling in this medium has progressed some much in last decade it out paces the anything a board game could offer.

In other words the target audience is leaving.

2.) Major of voters:  This variable is all about the amount of users who share their ranking.  A rule of thumb for rankings, reviews and ratings is those who go through the effort of expressing their opinion either love or hate the product.  The upper and lower confidence levels mirror themselves, because of this skew-ness.

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Next, I’ll discuss the positive relationship independent variables (focusing on those with the highest impact):

1.) Board games with an average game-play of at least two hours or more has the highest positive impact on a user rating a board game score above average.  What makes a game have a long game-play?

Multiple reasons: more players involved, more game-play mechanics, and mostly importantly more discussion.  The soul of any good board game is bringing people together.

2.) The second highest impact comes from the average score displayed from Game Board Geek.  The reason behind this is users see this rating first before submitting their rating.  Think of it like the Rotten Tomatoes effect, people want to feel like they have universally accepted opinions.  Take the beginning of this data story for example, I mentioned Monopoly is a family tradition of mine, this potentially could have swayed your opinion on this board game.  Possibility you could rate this higher than a game, say is fantasy themed, based on this model output.

For your own reference, this model has an accuracy rate of above 70%

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What have we learned from diving into the Board Game Data? 

Board games are most successful when they encourage the spirit and soul of “game night”, a gathering of friends and family discussing and enjoying each other’s time.  Adventure and exploration themes are the majority of the top ten highly successful board game genres.  The longer the game-play does not mean the game is like pulling teeth or the pace is slow.

It is more of an indicator of the amount of players required and the story telling the game has in driving a great game night experience.

 

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


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