Sam Edgemon is a Consultant in the SAS US Business Analytics Consulting Practice, and is responsible for supporting both pre-sales and post-sales engagements. With over twenty years of SAS Product and JMP Experience, project roles range from that as a contributing analyst to project lead and all aspects of managing technically oriented tasks. Experience comes from many areas: Government, Environmental, Biological Surveillance, Health Care, Pharmaceutical, Automotive, Financial Services, Education, Gaming, Sports, Recreation, and Agriculture. Sam holds a B. S. in Mathematics and a B.S. in Statistics from the University of Tennessee, and certifications in Data Science (Johns Hopkins University), Social Physics (M.I.T.), Innovation and Design (University of Virginia), and Sabermetrics (Boston University).
In a previous post, I wrote how pedigree might be used to help predict outcomes of horse races. In particular, I discussed a metric called the Dosage Index (DI), which appeared to be a leading indicator of success (at least historically). In this post, I want to introduce the Center
You may have heard that a horse named Nyquist won the Kentucky Derby recently. Nyquist was the favorite going into the race, though he was not without his doubters. Many expert race prognosticators questioned his stamina, and I was curious about the basis for those comments. My due diligence revealed
The Los Angeles Times recently produced a graphic illustrating the 30,699 shots that the recently retired Kobe Bryant took over the span of his 20-year career. It became such a topic of conversation that the Times later offered the graphic for $69.95 (plus shipping). The paper also published a follow-up
In my previous post, I showed how we explored the eras of baseball using a simple scatterplot that helped us generate questions and analytical direction. The next phase was figuring out how I might use analytics to aid the “subject matter knowledge” that had been applied to the data. Could
In consulting with companies about building models with their data, I always talk to them about how their data may differentiate itself over time. For instance, are there seasons in which you might expect a rise in flu cases per day, or is there an economic environment in which you