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Tag: Probability

2016 Round 3 – Tips and Predictions Double Derby

Posted in Ratings, and Tipping

Only two weeks in and we can already see “the most even comp in years” narrative start to play out. There are 10 teams in…

2016 Round 2 – Tips and Predictions Home teams travel away

Posted in Ratings, Tipping, and Uncategorized

Week 1 proved to be a very successful round of tipping for the FFSS (Figuring Footy Scoring Shots) predictor. 8 out of 9 games were tipped correctly, and if you followed through on my recommended bets you would have netted yourself a tidy little profit of +13.02 Units (which would be about $130 if you started with a total bankroll of $1000). However, I also wrote this week about grounding our expectations in reality, and why last week’s results may have been a bit lucky.

This week poses us a fresh new challenge. As has been discussed a fair bit on Twitter, last round saw every home team get up. With the way the AFL draw is organised in the early rounds, this means that every single one of Round 1’s winners, goes into Round 2 as the away team. This means that each of them will face a handicapping by FFSS’s Home Ground Advantage calculator. HGA takes into account distance travelled by each team, ground experience by each team and the AFL designated home team (for games that are played at shared stadiums). This week, HGA varies between ~7 ratings points for Essendon at the G against Melbourne and ~110 ratings points for Freo at home to the Suns.1

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?