With the 2024 Men’s AFL season just weeks away, I thought it timely to look at different perspectives of how teams have historically performed in home and away seasons from one season to the next.

I was thinking about the Strength of Schedule metric used in this blog from yesterday, and it struck me that, rather than using the raw values of the opponent team’s MoSHBODS rating and (for some metrics) the net Venue Performance Values (VPVs) for a game, we could, instead, convert these numbers into a win probability, which might make the resulting aggregate Strength of Schedule value more readilly interpretable.

The men’s AFL fixture for 2024 was recently released and, once again this year, we’ll analyse it to see what it means for all 18 teams.

The men’s AFL fixture for 2023 was released earlier this week, and tradition requires that the MoS website publishes its assessment of which teams fared best and which worst in that fixture given what the MoS models think about relative team strengths and venue effects.

This week I’ve been investigating the use of the Skellam Distribution in modelling AFL scores. That distribution can be derived as the difference between two, correlated, Poisson variables, which potentially makes it useful in an AFL context for modelling differences between team metrics.

Every year there’ll be a game or twelve where the fans of the losing team lament a lop-sided freekick count and largely attribute their team’s loss to the ineptitude (or worse) of the officiating. This will especially be the case where the visiting team has been on the thin end of the freekick count.

Commentators are keen to point out how especially important they feel are the minutes just before a change - especially before the final change - so today we’re going to investigate how our in-running estimate of a team’s victory probability at three-quarter time should be influenced by any streaks of scoring leading up to the break.

I thought that this year I might not have time to perform the traditional Strength of Schedule analysis, having spent far too long during the off-season re-optimising the MoS twins (more on which in a future blog), but here I am on a rainy night in Sydney with a toothache that a masochist would label ‘unpleasantly painful’ and the prospect of sleep before tomorrow’s 11:30am dental appointment fairly remote. So, let’s do it …

A few years back, I authored a blog post in which I deftly presented the case for the superiority of MAE over Accuracy for identifying the most talented forecasters.

Last season, we had the first partially player-based forecaster, MoSHPlay, which provided forecasts for game margins and game totals, and estimates of home team victory probabilities. It performed reasonably well in a year that was, in many ways, completely unlike those it had been trained on.

It used as inputs the margin and team score forecasts of MoSHBODS, and player ratings derived from historical SuperCoach scores.

In this blog I’ll take you through the process of reviewing the existing MoSHPlay forecasting equations, and investigate the efficacy of deriving player ratings from AFL Player Rating scores rather than from SuperCoach scores.

It feels slightly surreal that it’s only been a little over 12 months since last we did it, but it’s time to, once again, review the schedule for the men’s footy season ahead.

Perhaps naively in retrospect, I had hoped that the changes I made to the MoS twin algorithms in preparation for the 2020 season would be the last I’d need to make for a while. But, the unusual nature of 2020 fixturing highlighted some characteristics of both Systems that I thought needed redressing, so 2021 will see new versions of MoSSBODS and MoSHBODS providing key forecasts.

Even as a casual punter, you’d probably be aware of the availability of same-game multis which, put simply, allow you to construct a wager out of multiple “legs” (individual bets), usually up to some limit.

Sometimes, data comes at you fast …

This week the topic of what characteristics of a game of men’s AFL football might be correlated with its attendance was discussed on Twitter, which caused me to review this blog from 2015 where I looked at the same topic.

The fine folk who brought us the fitzRoy R package have been diligently collecting historical AFL Player Rating data with a view to potentially including it in an upcoming version of the package, and asked me to take a look at what they have so far, which spans the period from 2012 to the end of 2019.

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.