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

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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.

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

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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.

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Optimising the Very Simple Rating System (VSRS)

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In the previous blog, introducing the VSRS, I provided optimal values for the tuning parameters of that System, optimal in the sense that they minimised either the mean absolute or the mean squared error across the period 1999 to 2013
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To Win A Grand Final You Must First Lead

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History suggests that, as the higher-Rated "Home" team, Hawthorn must lead early and lead well if it is to be confident of success in Saturday's Grand Final, and not assume that its superior Rating will allow it to come back from any substantial deficit.
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Do Bookies Undervalue Team Performance Metrics?

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In 2003 Michael Lewis' Moneyball was published, in which he related the story of Billy Beane, Oakland A's General Manager, and his discovery that the market for baseball players mispriced particular skills. Some skills that could be shown, statistically, as being associated with greater team success weren't recognised as valuable (for example, getting on base, as measured by On-Base Percentage), while other skills were over-valued because of an historical belief that they were related to success (for example, batting in runs, as measured by RBI).
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Measuring the Surprise in a Season's Results

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In the previous blog we looked at the average level of surprisals generated by teams and by team pairings across all of VFL/AFL history and during the most-recent seasons. Today, as promised in that blog, I'm going to analyse surprisals using the same general methodology, but by season.
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In-Running Models: Confidence Intervals for Probability Estimates

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In a previous blog on the in-running models I generated point estimates for the Home team's victory probability at different stages in the game under a variety of different lead scenarios. In this blog I'll review the level of confidence we should have in some of those forecasts. More formally, I'll generate 95% confidence intervals for some of those point forecasts.
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Characterising AFL Seasons

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I can think of a number of ways that an AFL season might be characterised but for today's blog I'm going to call on a modelling approach that I used back in 2010, which is based on Brownian motion and which was inspired by a JASA paper from Hal S Stern.
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Does Crowd Size Affect Game Outcomes?

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Based on empirical evidence we know that there is a home ground advantage in AFL which, in part, might be attributable to the pro-Home team leanings amongst the majority of the crowd. In this blog I want to explore a slightly different question about the effects of the crowd: specifically, does the size of the crowd matter too?
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Deconstructing The 2011 TAB Sportsbet Bookmaker

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To what extent can the head-to-head prices set by the TAB Sportsbet Bookmaker in 2011 be modelled using only the competing teams' MAFL MARS Ratings, their respective Venue Experiences, and the Interstate Status of the fixture?
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An Empirical Review of the Favourite In-Running Model

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In the previous blog we reviewed a series of binary logits that modelled a favourite's probability of victory given its pre-game bookmaker-assessed head-to-head probability and its lead at the end of a particular quarter. There I provided just a single indication of the quality of those models: the accuracy with which they correctly predicted the final result of the game. That's a crude and very broad measure. In this blog we'll take a closer look at the empirical model fits to investigate their performance in games with different leads and probabilities.
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Hanging Onto a Favourite: Assessing a Favourite's In-Running Chances of Victory

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Over the weekend I was paying particular attention to the in-running odds being offered on various games and remain convinced that punters overestimate the probability of the favourite ultimately winning, especially when the favourite trails.
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Win Production Functions for AFL Teams - 1897 to 2010

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Right now I'm reading Wayne L Winston's Mathletics, a book about the use of fairly simple mathematics and sports statistics to gain insights into the results of American sports. Inspired by this book, in particular by a piece on Pythagorean Expectation which relates the season-long winning percentage of a baseball team to the total runs that it's scored and allowed, I wondered if an AFL team's win percentage could be similarly predicted by a handful of summary statistics about its own and its opponents' scoring.
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Home Ground Advantage: Fans and Familiarity

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In AFL, playing at home is a distinct advantage, albeit perhaps a little less of an advantage than it once was. So, around this time of year, I usually spend a few days agonising over the allocation of home team status for each game in the upcoming season.
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Why It Matters Which Team Wins

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In conversation - and in interrogation, come to think of it - the key to getting a good answer is often in the framing of the question.

So too in statistical modelling, where one common method for asking a slightly different question of the data is to take the variables you have and transform them.

Consider for example the following results for four binary logits, each built to provide an answer to the question 'Under what circumstances does the team with the higher MARS Rating tend to win?'.
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Drawing On Hindsight

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When sports journos wait until after a contest has been decided before declaring a group of winning punters to be "savvy", I find it hard not to be at least a little cynical about the aptness of the label.

So when, on Sunday, I read in the online version of the SMH that a posse of said savvy punters had foxed the bookies and cleaned up on the draw, collectively winning as I recall about $1m at prices ranging from $34 to $51, I did wonder how many column-inches would have been devoted to those same punters had the margin been anything different when the final siren sounded on Saturday. I'm fairly certain it would have been the number that has '1' as its next-door, up the road neighbour on Integer Street.
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