# NFL: Predicting 2018 Win Totals with Data Science

With the Super Bowl just behind us, it’s time to predict wins for the 2018 NFL Season.  At the start of the playoffs, we looked at a model which predicted how many games NFL teams should have won in 2017 and compared our results to Football Outsider’s Pythagorean Win Expectancy.  We were able to improve on Pythagorean Win Expectancy for last year’s results, aka how many games a team should have won, but our backwards looking models were unable to beat Pythagorean Win Expectancy in predicting next year’s wins.  Today, we will build some models trying specifically to predict how many games teams will win next year.

If you simply want to know how many games your team will win in 2018, strictly for recreational purposes of course, you can skim to the end or check out our Spotfire Template.  But, for Football Outsiders fans, those interested in what makes up wins and losses, or those interested in the Data Science process, read on.

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.

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.

# 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.

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.

# Real Estate Secrets: Hidden Trend Visualization

Everyone who has ever owned or lived in a house knows at least a little bit about the whims of the real estate market. Big houses cost more, neighborhood matters, proximity to basic services is great, age and style are important in some markets, you name it. But what is it that matters the most? This is a question that visualization can help us answer.