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

012


001.png


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.
015

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.

002

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.

003


 

Video Game Recommendation Engine – The Output

004.png

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.

005


Video Game Debate: Overview

006

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

007

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

008

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

009

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

010

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

011

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:


012


013


014


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.


003_008

Classification Tree, Game of Thrones, Tree Based Models

Recipe: 009 Game of Thrones Survival of the Fittest

logo


“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!


001


002


003

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

004


005

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.

006


007

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.


008

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!


006

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


005

009


003_008

Classification Tree, Harry Potter, Tree Based Models

Recipe: 003 Harry Potter: Did Voldemort Get-cha? Classification Tree

 

logo“It does not do well to dwell on dreams and forget to live.” – Albus Dumbledore – Harry Potter and the Sorcerer’s Stone

In this post we won’t dwell but we’ll analyze and learn.  I ask that you play along and imagine yourself receiving your acceptance letter to Hogwarts (well let’s be honest here we’ve all imagined this at one point or another).

So you’ve hopped off the Hogwarts’s Express, ready for your studies and the fight the dark arts. Oh wait… nobody told you about the dark arts and all the threats looming your way? Ever wonder was the budget only allowed for owls to deliver acceptance letters? This week we’ll dive into the greatest threat in the Harry Potter Universe, Lord Voldemort.


003_001


003_002


003_003


003_004


003_005


003_006


003_007


003_008