Data science

Some folks like to look at current polls and poll averages and make a mental adjustment to the results and see how they’d look if the polls missed the way they did in the recent past. But I think even for people who follow this stuff pretty closely, it can be hard to remember all those details. My goal here is to help with that kind of thing while also showing that the narrative about polling errors is probably an overgeneralization.

Some folks like to look at current polls and poll averages and make a mental adjustment to the results and see how they’d look if the polls missed the way they did in the recent past. But I think even for people who follow this stuff pretty closely, it can be hard to remember all those details. My goal here is to help with that kind of thing while also showing that the narrative about polling errors is probably an overgeneralization.

When you want to find out who the NFL’s best kickers are, the tools available to you tend to be pretty crude, with not much more than a record of makes and misses. We all know from watching the games that some kicks are harder than others, but how do we synthesize that into a few easy to understand statistics?

Here I discuss statistical and logistical details involved in my attempts at modeling kicker talent. This includes model selection as well as a brief history of research. I use some quantitative assessments to compare my approach to previous attempts and it does quite well.