Modelling Game Outcomes In-Running

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Way back in 2010 I developed a model to estimate the Home team's chances of victory in-running (ie during the course of a game) based on the lead it held at the time and the time remaining in the game. In a subsequent post I investigated ways of combining the in-running projections of that model with the TAB Bookmaker's pre-game assessments in something of a proto-Bayesian way.

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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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Revisiting Home Ground Advantage

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This week I've been part of a Twitter conversation about Home Ground Advantage in the AFL, a trending topic because of the shift from Football Park to Adelaide Oval for the home games of Adelaide and Port Adelaide in the 2014 season.
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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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The Relative Importance of Class and Form in AFL

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Today's blog is motivated by a number of things, the first of which is alluded to in the title: the quantitative exploration of the contributions that teams' underlying class or skill plays in their success in a given game relative to their more recent, more ephemeral form. Is, for example, a top-rated team that's been a little out of form recently more or less likely to beat a less-credentialled team that's been in exceptional form?
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Is Class More or Less Important In AFL Finals?

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You'll hear it said about sport that class emerges when it's needed most. If that applies to football then you'd expect that better teams would be more likely to win games in the Finals than they are games in the regular home-and-away season.
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The Predictability of 2013

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Friend of MAFL, Michael, e-mailed me earlier to ask about my claim that 2013 was on track to be the most predictable MAFL season ever, pointing out, quite correctly, that bookmaker favourites have been winning at about the same rate - perhaps even at a slightly higher rate - as they had been at the same time last year.
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Game Statistics and Game Outcomes

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My first Matter of Stats blog looked at how game statistics, averaged across an entire season for each team, are predictive of key season outcomes like ladder position, competition points and MARS Ratings. This post summarises similar analyses, but here performed on a per-game basis
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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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Which Teams Fare Better as Favourites?

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In this blog, the next is a series in which I've been exploring the all-time MARS Ratings I created for every game from the start of 1897 to the end of the 2012 season, I'll be looking at how well each team has performed depending on the relative strength of its opponent, as measured by their MARS Rating. So, for example, we'll consider how well Collingwood tends to do when playing a team it is assessed as being much stronger than, a little stronger than, about as capable as, and so on.
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Building Simple Margin Predictors

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Having a new - and, it seems, generally superior - way to calculate Bookmaker Implicit Probabilities is like having a new toy to play with. Most recently I've been using it to create a family of simple Margin Predictors, each optimised in a different way.
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Does an Extra Day's Rest Matter in the Home and Away Season?

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Whenever the draw for a new season is revealed there's much discussion about the teams that face one another only once, about which teams need to travel interstate more than others, and about which teams are asked to play successive games with fewer days rest. There is in the discussion an implicit assumption that more days rest is better than fewer days rest but, to my knowledge, this is never supported by empirical analysis. It is, like much of the discussion about football, considered axiomatic. In this blog we'll assess how reasonable that assumption is.
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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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The Increased Importance of Predicting Away Team Scores

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In an earlier blog we found that the score of the Home team carried more information about the final game margin than did the score of the Away team. One way of interpreting this fact is that, given the choice between improving your prediction of the Home team score or your prediction of the Away team score, you should opt for the former if your goal is to predict the final game margin. While that's true, it turns out that it's less true now than it once was.
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