In several of my recent articles, I have been using Jeremias Engelmann’s 12 year average RAPM dataset. Here, I will look at what players may have been underrated or overrated by even advanced stats over the course of the last 12 years. I decided to use PER and WS/48 as my advanced stats to compare … [Read more…]
One of the big ongoing questions in NBA stats and player valuation is where to set “replacement level”. I believe an empirical approach to determining that level is best. Jeremias Engelmann has produced an outstanding resource in his 12 year average RAPM data. Since so many years are used, it is quite stable; however, one … [Read more…]
Here are my 2013 NBA Team Projections, based on my ASPM player projections. For playing time, I roughly used ESPN’s Fantasy Projections, tweaked so each team totaled 475% of the minutes (I assumed the rest of the minutes would go to replacement level (-3.25) players). Here are my regular season projections: I have updated these … [Read more…]
The NFL season begins tonight, and there are predictions flying everywhere. I thought I’d take a basic analytical approach and produce a baseline projection. If your predictions can’t beat this (dumb) system, you aren’t very good at predictions! What got me started down this trail was Bill Barnwell’s article on Grantland breaking down some good … [Read more…]
This is a completely non-sports post, but I had the data and visualization (created for my own use) and thought I should post it online. I have compiled, from the Hardware.fr series on return rates of computer components, a database of component return rates. Having that data, I, of course, felt the need to first … [Read more…]
Inspired by Evan Zamir’s use of a treemap to visualize WARP over at The City, I created a treemap to visualize ASPM VORP for the 2012 season-to-date. I used Google Docs and the treemap-gviz widget to construct the visualization. I used the latest iteration of the ASPM framework to compile the data, and selected for … [Read more…]
It’s time for the With-or-Without-You analysis of the NBA Finals teams: Dallas and Miami. For this review, I will consider a player to not have played (i.e. “Without You”) if the player played 20% or less of their average MPG. This allows us to look into the coach’s rotation decisions a bit more, while still … [Read more…]