# Competitiveness in the VFL/AFL (1897-2015)

It's been a while since we've reviewed the history of game margins and, in today's blog, we'll consider that history from a number of perspectives.

# The Ideal Competition: How Many Blowouts and Upsets?

Working on a few recent posts here on the Statistical Analysis journal has made me think a lot more about blowouts (games won by a large margin) and upsets (games won by the team less-favoured to win pre-game), and realise how inter-related is their prevalence.

# Why There'll Always Be More Blowouts Than We Expect

Last night I was thinking about the results we found in the previous blog post about upsets and mismatches and wondered if the historical pattern of expected game margins was borne out in the actual results. On analysing the data I found that there were a lot more victories of 10 Scoring Shots or more in magnitude than MoSSBODS had predicted. In most seasons, at least one-third of the games finished with a victory margin equivalent to 10 Scoring Shots or more, which was usually two or three times as many as MoSSBODS had predicted.

# Grand Final Leads - How Much Is Enough?

Quick question: what proportion of teams that have led at the end of the 1st Quarter of a Grand Final have gone on to take the Flag? Supplementary question: how big does the Quarter-time lead need to be before the probability reaches 90%?

Finals, by their nature, tend to pit more-evenly matched teams against one another, on average, than do games from the home-and-away season. It seems reasonable, therefore, to hypothesise that margins will tend to be smaller in Finals than in the home-and-away season, but what other changes in scoring behaviour might we expect to see?

In the previous blog here on Statistical Analysis I referred to this paper and applied its drift-free Random Walk model to the "safety" of leads recent AFL history, finding that, to some extent, it fitted empirical data well.

# Predicting Total Game Scores Versus Predicting Margins

In the comments section of the previous blog, LT pointed out that Bookmakers seem to be doing a better job this year predicting the sum of the Home Team and Away Team scores than predicting the difference between them.

# Estimating Game Margin Variability: Empirical Challenges

Lately I've been thinking a lot and writing a little - a mix that experience has taught me is nearer optimal - about the variability of game margins around their expected values.

# Ensemble Encore

The idea of ensemble learning and prediction intrigues me, which, I suppose, is why I've written about it so often here on MoS, for example here in introducing the Really Simple Margin Predictorshere in a more theoretical context, and, much earlier, here about creating an ensemble from different Head-to-Head predictors. The basic concept, which is that a combination of forecasters can outperform any single one of them, seems plausible yet remarkable. By taking nothing more than what we already have - a set of forecasts - we're somehow able to conjure empirical evidence for the cliche that "none of us is better than all of us" (at least some of the time)

# The Effects of Narrow Wins and Losses On Subsequent Performance

I've often heard it asserted after a team's close loss that it will "bounce back harder next week". With a little work, that's a testable claim.

# Who's Best? It's All About The Base(s)

Simple question: which of MoS' 17 Margin Predictors has been best-performed over the past two seasons?

# Attaching Probabilities to Game Margins: An Application of Quantile Regression

I first heard about quantile regression, I think, over a decade ago and, for whatever reason, could never quite understand it nor fathom a useful application for it here.

# More Ways to Derive Probability and Margin Predictions From Head-to-Head Prices

A couple of weeks ago, in this earlier blog, I described a general framework for deriving probability predictions from a bookmaker's head-to-head prices and then, if required, generating margin predictions from those probability predictions.

As I was writing up the recent post about the application of the Pythagorean Expectation approach to AFL I realised that it provided yet another method for generating a margin prediction from a probability prediction.

# The Ten Most Surprising Things I've Learned About AFL So Far

The last few months have been a generally reflective time for me, and with my decision to leave unchanged the core of MAFL algorithms for 2014 I've been focussing some of that reflection on the eight full seasons I've now spent analysing and predicting AFL results.

# The Dynamics of ChiPS Ratings: 2000 to 2013

Visitors to the MatterOfStats site in 2014 will be reading about ChiPS team Ratings and the new Margin Predictor and Probability Predictor that are based on them, which I introduced in this previous blog. I'll not be abandoning my other team Ratings System, MARS, since its Ratings have proven to be so statistically valuable over the years as inputs to Fund algorithms and various Predictors, but I will be comparing and contrasting the MARS and the ChiPS Ratings at various times during the season.