R

Choosing a Fantasy Football Kicker with Empirical Bayes Estimation

We’ll use 50 years of NFL kicking data to inform the least – or most – important decision of your fantasy season: Drafting a kicker.

Tidy Time Series Forecasting

Take your time series forecasting game to the next level by working through two real world scenarios in the Tidyverse!

The State of Names in America

In this post, we’ll leverage 110 years of historical data – and everything from time-series forecasting to hypothesis testing – to understand how one’s state of birth influences their name

Text Mining for the Perfect Beer

In this post, we’ll analyze reviews and ratings from Beeradvocate.com to understand what drives satisfaction amongst beer drinkers worldwide. Prost!

Surviving the NFL

Survival Analysis is the go-to method for analyzing time-to-event data. In this post, we’ll go deep on some historical player data and then leverage machine learning to predict how long new draft picks will remain in the NFL.

College Rankings and Pay

College rankings are a standard input for most students when choosing a school. But to what extent does a college’s rank relate to how much a graduate makes 10 years into their career? We’ll answer this question by web scraping data from a variety of online sources with R and Python, and then build a model to understand which factors matter most to post-college pay.

The Optimal Portland Pub Crawl

Portland, Oregon is home to some of the best watering holes in America. With so many places to quaff a West Coast Style IPA or glass of Pinot Noir, choosing which to visit (and in which order) can be a daunting task. To address this question, we’ll leverage some classic optimization techniques to minimize the total distance travelled between the top bars in Portland for a truly “optimal” Pub Crawl.

Computer Vision with R & Keras

Keras is quickly becoming the go-to prototyping solution for computer vision problems, and this post provides an overview of how to rapidly build a Convolutional Neural Network in R with the Keras library.

Time Series Forecasting with Neural Networks

Advanced machine learning algorithms like Artificial Neural Networks(ANNs) can’t model time-dependent data without some pre-processing. The additional processing hurdle often deters forecasters from implementing advanced methods in favor of classic (but less powerful) approaches. However, I’ve observed some notable accuracy gains applying ANNs to forecasting problems. Accordingly, this post provides a basic playbook for data cleaning, feature engineering, model selection, prediction, and risk assessment when forecasting with Neural Nets.

Choosing a Fantasy Football Quarterback

Aaron Rodgers or Tom Brady? Carson Wentz or Drew Brees? Choosing the right Fantasy Football QB each week is challenging. To remove some of the guesswork from the decision-making process, I devised an approach that’s worked well over the past few seasons. Read on to learn more about using the Beta Distribution to pick your weekly starting QB.