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.

In an earlier post, we built a predictive model for game margins using teams’ individual and shared experience data (gathered from the Fitzroy R package) to supplement the MoSHBODS forecasts.

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.

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.

Back in October of 2017 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.

For some time now I've opined that I don't like accuracy as a forecaster performance metric because it's "too lumpy", and that I prefer, instead, mean absolute error.

Today we'll use player data from the fitzRoy package in R to investigate the first names of players in the 15,402 games completed to the end of the first week of the 2018 Finals.

I've been talking here on MoS and elsewhere about doing some analysis of player data for quite some time but, until now, have lacked a key ingredient for that analysis (viz, the data). That's just changed.

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

Since the first VFL Grand Final of 1898 (there was none in the competition's first year, 1897) there have been 120 more, 117 of them providing an outcome and three finishing in draws and necessitating replays.

This week, while looking at the performance of the MoS twins and the TAB and Centrebet bookmakers, I noticed that all four had better mean absolute errors (MAEs) in their forecasts for Away teams compared to those for Home teams (see the bottom of this post).

Last year, the Western Bulldogs bucked recent Finals history by going on to win the Flag after finishing 7th in the regular home-and-away season.

The 2017 season has been a close one, with any team a genuine chance of dominating and maybe even toppling any other team on a given day. More than once, a team near the foot of the competition ladder has defeated a team near the top, and we sit here at the end of Round 20 with the final 8 far from decided.

In the previous blog we looked at the reduction in vigorish that arose from adding a single bookmaker to the portfolio of options when wagering on the AFL. There we found significant reductions in average vigorish from this simple inclusion.