Performance-Testing the In-Running Model Against 2017 to 2019 Data

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.

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Building and Performance-Testing an In-Running 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.

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Classifying Recent AFL Players by Position: Part 4 (How Many Player Types Are There?)

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.

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Update: Does An Extra Days' Rest Matter in the Finals

Back in 2012 I investigated the question of whether or not the number of days' rest that a team received in the Finals had any significant effect on its subsequent performance.

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.

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Player Experience Data: Analysis and Modelling

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.

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