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Another very disappointing week for the model. Although it tipped 6/9 this week it only got 3/9 on the line, with all the largest bets going against the model this week. With a bit more luck hopefully things will turn around soon!

A better week for the model from a betting point of view with the model predicting significant bets on Essendon (mainly on the line), Western Bulldogs and Hawthorn. However, the model also predicted large bets on Richmond and Fremantle, which both lost, resulting in a small loss of 2% for the round.

From a prediction point of view the model was less precise than round 1, with an average margin of error of 34.2 points and it only predicted 4 winners. However, this was better than the bookmakers with only 3/9 favourites winning this week in what was an extremely hard week of tipping. The model also predicted the line correctly 6/9 times.

Below is the summary of the model’s performance for Round 1, 2016. Some key points:

• The model predicted 7/9 winners based on the final teams named in each game.
• The model predicted the winner on the line 4/9 times.
• The model made a loss of 15%, mainly due to big losses on Essendon and Brisbane on the line. Caution should be exercised when betting on Essendon in the first few rounds as the model probably overrates Essendon, given the WADA suspensions.
• The errors of the model were actually relatively small compared to the average, but the errors on the two largest bets (Essendon and Brisbane on the line) went against the recommended bet.
• The predictions for total points (not shown) were poor, with much higher scores than last season. I will wait discontinue this model from now, at least until several rounds into the season.

Welcome to my website. I am a PhD student in Economics in Melbourne. Using statistical techniques I have built a model to predict AFL outcomes based on team and player quality. So far, this model has only been backtested, but has done very well.

This website will be updated weekly and before games with predictions of margin of victory, line probability (margin and total points) and win probability.

I will also post my tips prior to each round. Enjoy!

Below are my predictions for the 2016 season from my model based on 5,000 simulations. It contains each sides average ladder position, finals and top 4 probabilities, average and median number of wins for the season, and 95 % and 50% confidence intervals of wins for the season.

Best bets are in yellow.

From this it appears that the best bets are:

1. North Melbourne to make the top 4 and top 8.
2. Melbourne and St Kilda to make the top 8 and top 4.
3. Geelong and West Coast to make the top  8.
4. Western Bulldogs to make the top 4.

This will be updated before the start of the season.