Back in November of 2018 we looked at the then-upcoming season of the AFL and estimated the strength of schedule for all 18 teams based on the MoSHBODS Ratings and Venue Performance Values (VPVs) that prevailed at the time.

In this blog post we'll use the same methodology but replace the static, start-of-season MoSHBODS data with the dynamic Ratings and VPVs that each team's opponents carried into the respective game to assess who, in hindsight, had easier or more difficult schedules than we assessed initially.

This year we’ll use the same methodology as we did last year to analyse the AFL Fixture for the upcoming season. Details of that methodology are in this post from 2015 and this one from the year before. We’ll again use the MoSHBODS Team Rating System to provide the estimates of relative team ability and venue effects.

Momentum. It's something that you're almost guaranteed to hear mentioned at least once in any sporting commentary, and it's a topic that continues to pique my interest. Whether or not it exists remains a legitimate subject for debate, but detecting it, if it does exist, has been elusive.

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.

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.

I’ve created in-running models before, for the projected final total of a game in progress, as well as for the projected final margin and probability of victory.

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.

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.