About

The goal:

To use past football results to predict future football results. I will be publishing bets my model suggests ahead of time each week. The information is provided for free, and anyone may use it however they like (except for selling it for personal gain). There are websites that will charge you as much as $1500 per year for suggestions such as those provided here free of charge. And their performance is inferior to this model’s performance! Outrageous.

If you don’t like betting, why not use my tips in your work tipping competition?

If you want to get into sports betting but don’t trust me (fair enough), follow my suggested bets for a few weeks or months to see how they perform. Use them at your own peril, as I cannot be held accountable for gambling losses. Only bet as much as you can afford to lose.

Who am I?

I am a Ph.D student at the University of Melbourne studying astrophysics. I have applied my knowledge of gravitational lensing, dark matter and supernovae to Australian football.

Got questions?

Please contact me!

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One comment

  1. Hi Rob

    Great work with your predictions site, I am always following the player model predictions. I took on your advice into working out team’s score as a starting point. I first began by running a regression analysis on the contribution of various stats on a team’s total points for and against, however ultimately this only gave me a high R squared rating only when goals for were considered. Therefore I have taken a much more simpler approach and predicted a teams score based on their average points for then adding or subtracting the difference between the league average points against and the opponents points against. E.g. Carlton have a 72 point average for and league points against is 85, then when Hawthorn who have a points for of say 100 play them they will get an extra 13 points meaning Hawthorn’s predicted score if they were to play carlton would be 113. I have also stuck by my initial approach in running a regression analysis on R50/I50, MarksI50/I50, Scoring Shots/I50, Ave Goals For, Ave Goal Assists For, Ave Goal Assists against, Ave Goals against, MI50/RB50conceded, Clearances conceded/Tackle made, to determine the effect of these stats on the number of games each club has won since 2009. Therefore when I apply the regression formula to today’s stats, it will give me a prediction of the no. of games a team wins in the next 5 years or so. From this I use this figure and divide it by the figure plus the oppositions figure to give me a percentage. This has been reasonably accurate in terms of picking winners, and even picking some upsets, while at times it does line up with your player models. However, I know it doesn’t actually factor in the opposition. Now what I am confused about is how it is you rate a player’s impact on the game, like how is it you actually quantify it into a stat. I am trying to find a starting point to quantify say Nat Fyfe’s impact, like how do the number of clearances he wins per game affect a team’s score or chance to win.

    I know in Baseball, they have a stat called WAR(Wins above Replacement) and that say a pitcher had a WAR of 6, and the team pitching WAR was 4, the pitcher has an additional WAR of 2(in MLB starting pitchers play once every 4-5 days or so, therefore a team may have up to 5 different starters). I have also read that the WAR number multiplied by 10 gives you the amount of runs contributed or prevented by a player. E.g. a team pitch war is 4, and a starter WAR is 6, it would mean if the player played out the entire season (every game) he would save the team 20 runs. Therefore we can judge his impact on a game based on that stat. Similarly, a hitter with a War of 8 to team hitting war of 10, would cost the team 20 runs over the course of the season if he played every single game of the season.
    This WAR stat is readily available stat, and is made up of various factors.

    So back to our great game, I am trying to figure out how to quantify a player’s individual performance and contributions. We all know that a Nat Fyfe or Gary Ablett (when Fit) would probably be of much more value as opposed to a Dennis Armfield. . So how is it that you quantify this. If you could point me in the right direction with some tips that would be greatly appreciated.

    Sorry for the long message, kind of got too into it.

    Thanks for your help,
    Jev

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