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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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.

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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

propb

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

Change in air acceleration

Run speed

The lowest influencers were:

Base air acceleration

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

Drum roll please….

main1

main2

main3

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

Really looking forward to the comments section on this one 🙂


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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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