Winning Scores and Underdogs' Chances

/

Recently, I noted, somewhat in passing in this piece on close game and blowouts, the decline in overall team scoring, a topic that's receiving not a little attention within the football community at the moment, fuelled partly by some recent low-scoring games, in particular the Dees v Lions encounter.

Read More

On Choosing Strong Classifiers for Predicting Line Betting Results

/

The themes in this blog have been bouncing around in my thoughts - in virtual and in unpublished blog form - for quite a while now. My formal qualifications are as an Econometrician but many of the models that I find myself using in MoS come from the more recent (though still surprisingly old) Machine Learning (ML) discipline, which I'd characterise as being more concerned with the predictive ability of a model than with its theoretical pedigree. (Breiman wrote a wonderful piece on this topic, entitled Statistical Modelling: The Two Cultures, back in 2005.)

Read More

Clustering Your Way To Line Betting Success : Building a Predictive Model

/
In the previous blog I used a clustering algorithm - Partitioning Around Medoids (PAM) as it happens - to group games that were similar in terms of pre-game TAB Bookmaker odds, the teams' MARS Ratings, and whether or not the game was an Interstate clash. There it turned out that, even though I'd clustered using only pre-game data, the resulting clusters were highly differentiated with respect to the line betting success rates of the Home teams in each cluster.
Read More

Clustering Your Way To Line Betting Success

/
For today's blog I'll be creating a game clustering that uses as input only the information that we might reasonably know pre-game - for example, the pre-game team MARS Ratings, Bookmaker prices (or some metric derived from them), and information about the game venue.
Read More