A pretty good round I think. My models seem to be under-predicting margins, something I didn’t really pick up until I started looking at individual games. Having said that, my probabilities are tending to be higher than other models around and that really helped my BITS score.
Perhaps the volatility estimation for player/team performances I’m using is not optimal and I’m getting a skinnier bell curve of simulated results than others. We shall see!
This year I’ll be focussing on tweaking my model, sussing out its strengths and weaknesses and measuring it up against others. Although I have simulated data from the first 10 rounds, that was simulated blind to the actual results, I will be measuring it against results only from this round onwards; just in case my slightly messy code managed to have prior knowledge.
Welcome to the AFL Lab. This project is part of my ongoing education in data analysis. I love footy and numbers, so why not combine the two? I have a strong mathematical background but I’m comparatively weak on the statistics side. This is my attempt to rectify this, in a very reckless and un-rigorous way.
Normally when approaching a problem it is standard practice to start with something simple and add complexity (Occam’s Razor?), but I have gone all-in, throwing stats haphazardly at scikit-learn models. Will it work or will it explode?
My formulation is currently very unrefined, with many parameters (and probably way too many parameters) yet to be tweaked. Nevertheless, having simulated Rounds 1-10, 2018, my model has tipped 63, average margin 28.5 and a bits score of 16.61. According to the Squiggle leaderboard as of today, the leading model is on 62/28.17/14.58.
The model is not completely ready yet (it’s about 5 tips behind in a simulation of 2017), but it’s doing something right. So over the next few weeks I might write a few things about my modelling process and I’ll post round predictions/reviews and any other little fun bits I’ve found.
I’ll probably post a bit more frequently on Twitter at @AFLLab