MoSHBODS has tipped the Tigers to win by about 16 points on Saturday, but Giants supporters shouldn’t be too concerned about that, because MoSHBODS’ record in Grand Finals since 2000 isn’t particularly impressive, as you can see from the table below.Read More
In the previous blog, we created a quantile regression model that allowed us to estimate, in-running, a home team’s victory probability, and to create in-running confidence intervals for the home team’s final margin.
We evaluated that model based on a variety of performance metrics calculated using a 50% holdout sample from the original data set, which included games spanning the 2008 to 2016 period.
But nothing really measures a model’s performance better than a completely fresh data set from a non-overlapping time period, and in this blog we’ll be running the same metrics, but for games spanning the 2017 to 2019 period (up to and including the first week of the 2019 Finals). That’s 616 games entirely unseen by the model.Read More
For today’s blog I’m going to revisit that earlier model I built to project the final margin and estimate the home team’s probability in-running, with a view to being clearer about how the model was built, and how we can assess its efficacy.Read More
A few months back I had a first look at incorporating player data into predictive models, and found that we could knock about 0.4 points per game off the mean absolute error (MAE) of game margin predictions across the 2011 to 2018 seasons by valuing players solely on their Super Coach (SC) scores.Read More
In today’s blog post, the fourth in a series that started with this one, we’ll take the self-organising map that we’ve been using in Parts 2 and 3 and rework it to provide one answer to the question of how many distinct position types there are. The AFL Ratings site implicitly posits 7 distinct types, but the data might suggest otherwise.Read More
The dynamic and free-flowing nature of AFL, along with the wide-ranging abilities of some of its players, can make it difficult to categorise any single player as strictly and always playing in a defined position.Read More
With the 2018 AFL season completed, we’ve now a little time to think about how team and player data might be combined into a predictive model, and to estimate how much better such a hybrid model might perform than one based on team data alone.Read More
This year's Finals scheduling has made this topic highly relevant again, so it seems timely to update that analysis to include data from the intervening years and to incorporate some of the improvements I've made to estimating team ratings in that same period.Read More
Some of you will already know about the fantastic Fitzroy R package, which makes available a slew of match and player data spanning the entire history of the V/AFL. A number of people are already doing interesting things with that data, and this will be the first in what I expect will be a series of blog posts from me doing my own investigations and analyses with that data.Read More
I've been talking here on MoS and elsewhere about doing some analysis of player data for quite some time but, until now, have lacked a key ingredient for that analysis (viz, the data). That's just changed.Read More
Since the first VFL Grand Final of 1898 (there was none in the competition's first year, 1897) there have been 120 more, 117 of them providing an outcome and three finishing in draws and necessitating replays.Read More