Over the past few blogs (here and here) I’ve been investigating different methods for untangling skill from luck in forecasting game margins and, in this blog, we’ll try another approach, this time using what are called xScores.

One source of randomness in the AFL is how well a team converts the scoring opportunities it creates into goals versus behinds. Given enough data, analysts far cleverer than I can estimate how often a shot of a particular type taken from a particular point of the field under particular conditions should result in a goal, a behind, or no score at all.

So, we can adjust for that randomness in conversion by replacing the result of every scoring opportunity by the average score that we would expect an average player to generate from that opportunity given its specific characteristics. By summing the expected score associated with every scoring opportunity for a team in a given game we can come up with an expected score, or xScore, for that team.

For this blog, I’ll be using the xScores created by Twitter’s @AFLxScore for the years 2017 to 2020, and those created by Twitter’s @WheeloRatings for the years 2021 to 2024.

Let’s look firstly at the season-by-season Squiggle results of using, as a game’s margin, the xScore margin instead of the actual margin.

In the previous blog, I compared Squiggle forecasters’ actual margin prediction MAE results with a distribution of potential MAE outcomes from the same forecasts across 10,000 simulated 2024 season as one way of untangling the skill and luck portions of those actual results.

Those simulations require us to select “ground truth” for the underlying expected margin in each game. In the previous blog we used bookmaker data with an added random component of a Normal variable with mean 0 and standard deviation 8 as that ground truth.

The Squiggle website is a place where forecasters can post their forecasts for the winning team and winning margin, and provide probability estimates for upcoming games of men’s AFL football, and see how well or otherwise they perform relative to other forecasters. The only criteria for posting there is that the forecasters must have a history of performing “reasonably” well, and must not include any human-related inputs such as bookmaker prices in their models.

It’s been running since 2017 and, since 2018, has included a derived forecater, named s10, which is a weighted average of the 10 best Squiggle models, based on mean absolute margin error, from the previous season. The MoS model had been included in s10 in every year from 2018 to 2024, but will be absent in 2025 due to a relatively lowly 22nd place finish.

In this blog, among other things, I want to get a sense of the extent to which that apparently below-average performance might be attributed to skill versus luck.

More than once here on the MoS website we’ve looked at the topic of favourite-longshot bias (FLB), which asserts that bookmakers apply a higher profit margin to the prices of underdogs than they do to favourites. In one MoS piece (15 years ago!) I had more of a cursory look and found some evidence for FLB using 2006 to 2008 data, and, in another piece, a few years later I had a more detailed look and found only weak to moderate evidence using opening TAB data from 2006 to 2010.

At this point I think it’s fair to say that the jury is still out on FLB’s existence, and waiting for more convincing evidence either way (and very unhappy at having been sequestered for 13 years in the meantime).

Bookmakers, love them or lose to them, are good at their basic job, which is accurately estimating the probability of outcomes, and they give clues about their probability estimates in the prices they set. The problem is, those clues are cloaked in profit.

There are no doubt a number of viable ways of doing this, but one obvious approach is to fit a logistic equation of the form shown at right.

This provides an S-shaped mapping where estimated win probabilities respond most to changes in expected margins when those margins are near zero. It also ensures that all estimated probabilities lie between 0 and 1, which they must.

I’ve used this form of mapping for many years with values of k in the 0.04 to 0.05 range, and have found it to be very serviceable. I’ve also previously fitted it to bookmaker data and found that it generally provides an excellent fit.

Almost 10 years ago I wrote a blog that, among other things, noted that the score progressions - the goals.behinds numbers at the end of each quarter for both teams - were unique for every game ever played, regardless of the order in which you considered the two teams’ score progressions, home first then away, or away first and then home, choosing at random for every game. At that point, the statement was true for 14,490 games.

It seemed pretty startling then but, as of the end of 2024’s Round 9, the statement is STILL true, and that’s now for 16,487 games. V/AFL games remain as snowflake-like as ever.

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.