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.

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.