Skip to content

Tag: Results

Tipping Results vs Expected Round Probabilities Why getting a perfect round of tipping is such a big ask, even for a computer.

Posted in Ratings, and Tipping

My new computer predictor algorithm FFSS (Figuring Footy Scoring Shots) fared pretty admirably in her first week of tipping, nailing 8 out of 9 possible results. Only a bit of Dangerfield-inspired magic late on Monday afternoon prevented a perfect start to the season.

Anybody who has read this blog before will know that before the upcoming round I publish expected win probabilities for each match to be played. These probabilities are calculated by looking at the FFSS ratings of both teams and accounting for the venue. For example, I gave Sydney a 75% chance of beating Collingwood at home. They of course went on to thump them by 80 points. So then, if Sydney were so good, why didn’t the model rate them even higher and know ahead of time that they would dispatch Collingwood with ease?