I like analyzing data – especially sports data. As I do so, I’m going to post some rankings, ratings, and predictions to document my progress and compare my results with those of other “experts.”
I created and started using The Ranker about mid-way through the 2016 college football season. I was encouraged by the initial results, so I transitioned after the football season to basketball and continued to develop, test, and train The Ranker. I’ve been quite pleased with the results from working on our first basketball season. After all, The Ranker was better at picking against the spread than ESPN’s BPI predictions.
To be clear, The Ranker is a computer model that I created and continue to test and tweak. Inspired by Jeff Sagarin’s ratings, the goal was to assign ratings to every team that can be used to predict the scoring margin of any given game. Basically, the team with the higher rating should win by a margin equal to the difference between the two teams’ ratings.
I adjust team stats to account for strength of opponents, mix in some linear algebra and genetic algorithms, and the Ranker cranks out the resulting team ratings. They’re not perfect, but I’m always testing, tweaking, and improving.
It’s been a fun effort. I’ve learned a lot, and I have some ideas that could possibly (hopefully) improve The Ranker’s performance.