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)
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
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.
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.
We end up with five unique clusters:
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.
Like the Jack of All Trades group but faster.
Fast in aerial attacks and the heaviest of the characters.
This group is the fastest and the lightest.
A 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
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
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
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:
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:
- Jack of All Trades
- Air Tanks
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.