In the previous blog we investigated the patterns of teams' Scoring Shot conversion rates in consecutive home-and-away games, observing that teams tend to convert at higher rates after a loss, and lower rates after a win.

Lately I've been thinking a lot about the predictability of teams' conversion rates - that is, the proportion of their Scoring Shots that they turn into goals.

More and more often now, I'm being offered interesting suggestions for analyses by followers on Twitter, and today's blog is another example of this.

With the Tigers toppling the Swans with a goal after the siren on Saturday night, one of my Twitter followers, a Swans fan, wondered if there was a way to mathematically operationalise the notion of a "bogey team" and, more importantly, were the Tigers such a team for the Swans?

In the previous post we looked at some simple models for projecting the final score of an AFL game based solely on the scores at the quarter-time breaks. I said there that I'd revisit that modelling, incorporating pre-game market information, providing that I could source it in large enough volume.

It's quarter time, you've an Unders bet with a 180.5 threshold (ie you're betting that the final aggregate score will be 180 points or fewer) and you've just seen 40 points kicked in the 1st Quarter. How comfortable should you feel with you wager?

The MoSSBODS Team Rating System, while far from perfect, seems, based on its margin predicting performance across VFL/AFL history, to be capturing something useful about the underlying abilities of teams. Which is good, because that's what it was designed to do ...

These days, I reckon I know what a good margin forecaster looks like. Any person or algorithm - and I'm still at the point where I think there's a meaningful distinction to be made there - who (that?) can consistently predict margins within 30 points of the actual result is unarguably competent. That benchmark is based on the empirical performances I've seen from others and measured for my own forecasting models across the last decade of analysing football. 

With the move into Overs/Unders wagering this season, I've taken a special interest in the history of aggregate scores over the past few weeks. In this blog we'll review that history from a team, era and team pairing viewpoint.

For today's blog, a simple assignment: investigate the historical relationship between team MoSSBODS Ratings at the start of the season and their subsequent ability to make the Grand Final, and to win it.

Many AFL fans, I reckon, would have a reasonably accurate internal model of what a good, average or poor crowd might be for a given contest.

Lately, while waiting for the competition to generate some new, meaningful new data to analyse, I've been looking at the history of VFL/AFL scoring, in particular Scoring Shot generation and Conversion Rates.

The empirical world is almost always more complex than the theoretical one, which makes life more interesting but statistical modelling more difficult. As I've noted before, swings and roundabouts ...

A simple question: how bad can a team have been in the previous season and still harbour realistic hopes of playing in the current season's Grand Final?

As the 2016 AFL season proper looms and the window for more leisurely analyses slowly closes, today we'll wander across the expanse of footy history this time using MoSSBODS Team Ratings to decide which of the 1,442 teams that have played VFL/AFL football have been the big improvers, and which the big decliners (well ... you find an antonym then).

In the previous blog here on the Statistical Analyses journal, I concluded that Scoring Shots provided, on average, more information about the relative strengths of competing teams than did final scores.

In the previous post here on the Statistical Analyses blog we revisited the topic of Scoring Shot conversion and found that it appears to be unpredictable at a team level across entire seasons. That result, coupled with an earlier one where we found conversion rates to be unpredictable for a given team in a given game (and with some conversations I've had on Twitter subsequent to the more-recent analysis) makes it hard to reject the null hypothesis that team conversion rates are generated in a manner that's indistinguishable from a random variable.

The topic of team conversion rates - the proportion of Scoring Shots that teams convert into goals - and their predictability has come up before here on MoS. 

There's really no answer to the question "Which was the best VFL/AFL team of period X?", but team rating systems like MARS and MoSSBODS provide at least evidence-based panels of candidate teams to argue about.