Surprisals and Probability Scores
/Each week, at round's end, when I comment on the Probability Predictors I find myself stuck for a unit to attach to their performance.
Read MorePredicting the Final SuperMargin Bucket In-Running
/On Friday night, while watching the progress of the Saints v Freo game knowing that Investors has a SuperMargin wager on the Saints to win by 20-29, I was wondering how to react to the changes in the scoreline as the game progressed. Should I want the Saints to lead early? By a little? By a lot? By about 5 points at Quarter Time and 10 points at Half Time?
Read MoreThe Increased Importance of Predicting Away Team Scores
/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.
Read MoreFinding Non-Linear Relationships Between AFL Variables : Alternative Measures to MIC
/The Maximal Information Coefficient (MIC) that we explored in the previous blog is not the only non-linear measure of the pairwise relationship between any two continuous variables.
Read MoreFinding Non-Linear Relationships Between AFL Variables : The MINER Package
/It's easy enough to determine whether or not one continuous variable has a linear relationship with another, and how strong that relationship is, by calculating the Pearson product-moment correlation coefficient for the two variables. A value near +1 for this coefficient indicates a strong, positive linear relationship between the variables in question, so that high values of one tend to coincide with high values of the other, and vice versa for low values; a value near -1 indicates a strong, negative linear relationship; and a value of 0 indicates a lack of any linear relationship at all.
But what if we want to assess more generally if there's a relationship between two variables, linear or otherwise, and we don't know the exact form that this relationship takes? That's the purpose for which the Maximal Information Coefficient (MIC) was created, and recently made available in an R package called MINER.
Read MorePicking the High-Scoring Game
/Here's a simple question: what are the characteristics of games that tend to produce the highest aggregate scores?
Read MorePredicting the Final Margin In-Running (and Does Momentum Exist)?
/Just a short post tonight while we wait for the serious footy to begin.
For this blog I've again called upon the services of Formulize, this time to find for me equations that predict the final victory margin for the Home team (which might be negative or zero) purely as a function of the scores at the various quarter breaks.
Read MoreOptimising the Wager: Yet More Custom Metrics in Formulize
/As the poets Galdston, Waldman & Lind penned for the songstress Vanessa Williams: "sometimes the very thing you're looking for, is the one thing you can't see" (now try to get that song out of your head for the next few hours ...)
Read MorePredictors Behaving Badly: Intransitivity Revisited
/If I told you that I prefer green to blue, and blue to red, you'd probably assert that, logically, I should prefer green to red. In technical terms you'd be demanding that my colour preferences exhibit the property known as transitivity - one of the axioms of expected utility theory.
Read MoreWhat's Easier - Predicting the Home or the Away Team Score?
/Consider the following scenario. You're offered a bet in which you can choose to predict the final score of the Home or of the Away team and your adversary is then required to predict the final score of the other team.
Read MoreSetting an Initial Rating for GWS
/Last season I set Gold Coast's initial MARS Rating to the all-team average of 1,000 and they reeled off 70 point or greater losses in each of their first three outings, making a mockery of that Rating. Keen to avoid repeating the mistake with GWS this year, I've been mulling over my analytic options.
Read MoreSpecialist Margin Prediction: Epsilon Insensitive Loss Functions
/In the last blog we looked at Margin Prediction using what I called "bathtub" loss functions.
For the current blog I've extended the range of loss functions to include what are called epsilon-insensitive loss functions, which are similar to the "bathtub" loss functions except that they don't treat absolute errors of size greater than M points equally.
Read MoreSpecialist Margin Prediction: "Bathtub" Loss Functions
/We know that we can build quite simple, non-linear models to predict the margin of AFL games that will, on average, be within about 30 points of the actual result. So, if you found a bet type for which general margin prediction accuracy was important - where every point of error contributed to your less - then this would be your model.
This year we'll be moving into margin betting though, where the goal is to predict within X points of the actual result and being in error by X+1 points is no different from being wrong by X+100 points. In that environment, our all-purpose model might not be the right choice. In this blog I'll be describing a process for creating margin predicting models that specialise in predicting within X points of the final outcome.
Read MoreA Well-Calibrated Model
/It's nice to come up with a new twist on an old idea.
This year, in reviewing the relative advantages and disadvantages conferred on each team by the draw, I want to do it a little differently. Specifically, I want to estimate these effects by measuring the proportion of games that I expect each team will win given their actual draw compared to the proportion I'd expect them to win if they played every team twice (yes, that hoary old chestnut in a different guise - that isn't the 'new' bit).
Read MoreMeasures of Game Competitiveness
/All this analysis of victory margins, and a query from Dan about a recent blog post, has had me wondering about victory margin as a measure of the competitiveness of games.
Within a given era - say 10 years or so - during which the average points scored per game won't vary by too much, victory margin seems to be a reasonable proxy for competitiveness, but if you want to consider a broader swathe of AFL history, it strikes me as being deficient.
Read MoreMargins of Victory Across the Seasons
/This year MAFL Investors will be taking on the TAB bookmaker in a new arena by attempting to pick the final victory margin for each game within a 10-point range. Having not wagered in this market I've no bedrock of intuitions - nor misconceptions - about it yet; I thought I'd start with a little historical analysis.
Read MoreCursory Mention of MAFL in New Scientist (Probably)
/At the start of the year, Michael Schmidt, creator of the Eureqa application, mentioned that Justin Mullins from New Scientist was researching for a piece on Eureqa. I dropped an e-mail to Justin, received a polite reply and thought little more of it.
Turns out the final article included this paragraph:
"Today, the algorithm is called Eureqa and has thousands of users all over the world, with people using it for everything from financial forecasting to particle physics. One person even uses it to analyse the statistics of Australian rules football games."
(Various people have cut-and-pasted the full article, for example Transcurve and Kurzweilai, and you can access the original content directly via the New Scientist site if you're willing to create a free subscription.)
I can't be completely certain, but it's more likely than not that the last sentence refers to MAFL.
It'd be nice if the reference was a tad more direct - say with a name or a URL - but then again it'd be preferable if any wider awareness of MAFL's existence came at a time when the Funds were making rather than losing money. So, swings and roundabouts ...
Explaining More of the Variability in the Victory Margin of Finals
/This morning while out walking I got to wondering about two of the results from the latest post on the Wagers & Tips blog. First that teams from higher on the ladder have won 20 of the 22 Semi Finals between 2000 and 2010, and second that the TAB bookmaker has installed the winning team as favourite in only 64% of these contests. Putting those two facts together it's apparent that, in Semi Finals at least, the bookmaker's often favoured the team that finished lower on the ladder, and these teams have rarely won.
Read MoreMARS Ratings : How Important Are Teams' Initial Ratings?
/It's been a few years since I chose the key parameters for the MARS Ratings System, which I selected on the basis that they maximised the predictive accuracy of the resulting System. One of those parameters - the percentage carryover of team Ratings from one season to the next - determines each team's initial MARS Rating for the season.
Read MoreDeconstructing The 2011 TAB Sportsbet Bookmaker
/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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