Author: Jason May

Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.

How many games should your NFL team have won this season?

How many games should your NFL team have won this season?  Everyone knows a lucky bounce here and a bad call there can have a significant impact on the win-loss bottom line.  Hard core fans of Sports Analytics would recognize this factor as the driver behind Pythagorean Win Totals, a statistic derived to measure true performance.  Today, we are going to look to see if we can beat Pythagorean Win Totals as a predictor for how many games a team won in a certain season. IE, how many games should your team have won.

Spoiler:  we can make a better predictor, but in a way that makes us re-evaluate our understanding of Pythagorean Win Totals.

If you simply want to know how many games your team should have won, you can go straight to our Spotfire Template.  But, for Football Outsiders fans or those more interested in what makes up wins and losses, read on.

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Memories from the Houston Astros World Series Championship

We interrupt this analytically, data focused blog to attempt a little tug at the heart strings.  After all, Ruths.ai is a Houston proud company, and we all went through Hurricane Harvey and the subsequent Astros World Series run that brought the city together.  While this article might not delve into analytics, its subject–the 2017 World Series Champion Houston Astros–certainly serves as a model for how an analytically focused enterprise should run.

This article first appeared Friday, November 17 at Astros County, written by myself, our resident Astros fanatic.

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CRISP-DM Modeling: Forward and Backward Selection

Welcome back everyone to our Analytics Journey series.  Those of us in Houston have been through a trying time, and our thoughts are with the community.  We will try to return to a semblance of normalcy by continuing where we left off in our journey.

With all of our hard work in understanding and preparing the data during previous steps of the CRISP-DM method–exploring data, choosing a model space, removing NULLs, removing Multicollinearity–it’s time to have some fun with the Modeling stage.  Today, we’ll look at an aspect of Multiple Linear Regression:  Forward and Backward Selection.

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CRISP DM Data Preparation: Finding and Counting NULL Values in Spotfire

Hello, good friends.  The next step in our Analytics Journey takes us to the second iteration of Data Preparation.  This is the third step of the CRISP-DM method.

Today, we are going to look at one of the most common data quality issues in Spotfire:  the NULL, aka missing values.  While there are many ways to address NULL values like imputation (a lesson for another day), the first step is simply identifying them. We will walk through Spotfire’s built in NULL identifier and also a more advanced TERR based method.

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CRISP-DM Data Understanding: Marking and Filtering

For the first Data Understanding stage installment in our Analytics Journey, we explored Simpson’s Paradox in the survival statistics from the Titanic to highlight why the Data Understanding stage proves so important in the CRISP-DM process.  This week, we will use the same dataset and demonstrate how Spotfire’s unique Marking and Filtering capabilities make the Data Understanding stage much more efficient and powerful.

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CRISP-DM Business Understanding: KPI Charts

As Rustin Cohle said in True Detective, “Time is a flat circle,” so welcome back to the beginning of our Analytics Journey!  Previously, we cycled through the CRISP-DM process from beginning to end, explaining the stages as well as the way we approach our Data Science life cycle at Ruths.ai.  We have strived to demonstrate the importance of melding the human element with quantitative rigor.  Now, we will re-iterate through the steps as all good analytics processes will do, looking for ways to strengthen our model.  This time through, we will move from the theoretical to practical with an eye towards enacting the stages in the real world.

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CRISP DM: Deployment

Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process. Previously, we looked at an overview of the methodology as a whole as well as the Business UnderstandingData UnderstandingData Preparation, Modeling, and Evaluation stages.  Next, we examine the final stage:  Deployment.

The.  Final.  Stage.  Now, we just have to turn this thing on and reap the rewards, right?

      

Unfortunately, Deployment does not just happen with the push of a George Jetson button.

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Marking, Filtering, and Limiting, Oh My!

To veteran Spotfire users, the distinction between Marking, Filtering, and Limiting might seem obvious; however, to an uninitiated member, some similarities might cause confusion. In fact, one often can obtain the same exact result using combinations of Marking, Filtering, and Limiting. All the methods allow the user to make a click in one area that affects other visualizations. All the methods in their own way highlight a subset of the data.

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