MAFL 2010 : Round 6

It's as if someone flicked a switch, which in a fashion someone did.

All six Funds have taken up their option to participate in the weekend's wagering, many of them with an alacrity that can only be exhibited by something inanimate that has no sense of the terror that can be induced by a large wager on a team at $1.01.

Cherishing Inconsistency Where It's Welcome

In an earlier blog we demonstrated the benefits of consistency in football - moderate benefits if the consistency came in the form of generating scoring shots with less variability than teams of otherwise similar ability, and significant benefits if it came in the form of converting more of those opportunities into goals.

Indeed, consistency's a characteristic that sports commentators reserve for their warmest - and often longest - soliloquys, and players and teams, once they've reached an acceptable level of performance, announce as though scripted that they're now "striving for consistency". So, surely, consistency is always a good thing, isn't it?

MAFL 2010 : Round 5 Results

That was a weekend that could easily the unwary into thinking there's something in this statistical modelling stuff.

Six wins from seven bets, with two of the wins coming from seriously unfancied teams, lifted the Heuristic Fund by almost 30% and left it up almost 41% on the season. Investors with the Recommended Portfolio are now, therefore, up over 4% on the season.

Modelling AFL Team Scoring : Part III

This is the third in a series of blogs (here are Part I and Part II) about modelling the scoring of AFL teams and, with the heavy statistical lifting out of the way, in this blog we can look at the practical uses of what we've discovered so far, which is that:

(1) Team scoring can be modelled by the Score Equation

Score = Number of Scoring Shots x Conversion Rate x 6 + Number of Scoring Shots x (1 - Conversion Rate)

(2) A team's number of scoring shots can be modelled by a lognormal distribution with mean determined by the team's strength relative to its opponent and by whether or not the match is a home game for either team, and with a standard deviation of 5.5 scoring shots.

(3) Teams will convert the scoring shots they produce into goals as if drawing from a binomial distribution. The average team will convert 53.64% of its scoring shots into goals.

Modelling AFL Team Scoring : Part II

This is the second in a series of blogs about modelling the scoring of AFL teams.

In the previous blog on this topic I introduced what I called the Score Equation, which represents a way of thinking about a team's score in any game, and is as follows:

Score = Number of Scoring Shots x Conversion Rate x 6 + Number of Scoring Shots x (1 - Conversion Rate)

Then I used empirical data for seasons 2006 to 2009 to show that the bookmakers' starting prices could be used to predict with reasonable accuracy the number of scoring shots that a team will produce in a given game, and that teams, regardless of the number of scoring shots they produce, generally convert about 53.64% of them.

Modelling AFL Team Scoring

Today's blog is the first in a series that will look at statistically modelling the scoring behaviour of teams in the AFL.

If you're profoundly reductionist about it, you can think about a team's footy score as being the product of the number of scoring shots it creates and the proportion of those scoring shots that it converts into goals.

MAFL 2010 : Round 5

Well I did expect the level of wagering activity to ratchet up this weekend compared to last, but I thought Hope would be contributing to at least some of that increase.

That's not the case though. Hope has reviewed the market offerings and decided to return to slumber for at least another week; Shadow has looked at the same market and taken out its metaphorical knife and fork, rediscovering its Round 3-style appetite for the punt.