In the last blog, I described the creation of a simulator that could be used to generate synthetic games meant to resemble actual games from the men’s AFL competition.

In the normal course of things, it would have taken me months to create a simulator that I was happy with, but the current situation has given me larger blocks of time to devote to the problem than would otherwise have been the case, so the development process has been, as the business world loves to say, “fast-tracked”.

The new version is somewhat similar to the one I wrote about in this earlier blog, but different in a number of fundamental ways, each of which we’ll address during the remainder of this blog.

This past week, in between some pieces of client work, I’ve been coming up with a workable methodology for creating a score-by-score men’s AFL simulator that will be as faithful as possible to the actual scoring behaviour we’ve observed in recent seasons.

Over the next few blogs I’ll be describing the process of building this simulator which, let me stress immediately, is not yet finished. From what I’ve been able to create so far, I think the general approach I’m following is viable, but we’ll only see just how viable when it’s done.

Over the past couple of blogs, we’ve been analysing historical scoring progressions to come up with archetypical game types in terms of the ebb-and-flow of the game margin.

To do that, we treated the score progressions as time series data and today we’ll do something similar with teams’ season-by-season historical MoSH2020 Team Ratings for the period 1970 to 2019, inclusive.

In the previous post we looked at classifying men’s AFL games on the basis of the score progression from the home team’s point of view.

Now it might be that you’re indifferent about whether it was the home or the away team that was leading at any point, but instead care about the size of the margin from the point of view of the team that eventually won.

In today’s blog we’re going to revisit the analysis of the previous blog, using the same data, but looking at it from the viewpoint of the winning teams.

(Thanks to Daniel from InsightLane for this idea. You can find all of the score progressions used here on the ScoreWorm page of his website.)

It’s been a while since I’ve felt I’ve had much time to do more than post the outputs of any football analyses to Twitter, but sheltering-in-place and something of a lull in my consulting work has left me with a bit of time to redress that.

The MoS twins - MoSSBODS and MoSHBODS - have both performed well these past few seasons, but there are some aspects of them that I’ve wanted to improve for a while, and I’ve decided that 2020 is the time to do that.

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.

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.

This is the third in a series of blog post that started with this one where we created a self-organising map of current AFL players based on publicly available game statistics, and overlaid the positions to which those players had been assigned on the AFL Player Ratings site.

There seems to have been some interest in yesterday’s blog post where we created a self-organising map of current AFL players based on publicly available game statistics, and overlaid the positions to which those players had been assigned on the AFL Player Ratings site.

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.

For quite a few years now I've used essentially the same methodology to analyse the AFL Fixture for the upcoming season (see this post from 2015, and, probably more relevantly, this one from the year before if you'd like the details).

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.