The Rugby World Cup final early on Sunday – 3am, AEDT – pitches the formidable New Zealand All Blacks, the world’s No. 1 side and heavy favourites, against the surprisingly successful Australian Wallabies.
The local betting market has the All Blacks at 1.40-1 hot favourites, significantly shorter than the 3-1 offered for a Wallabies victory.
And like punters, actuaries at QBE’s European division believe that the men in black will prevail. According to their QBE rugby predictor, which uses stochastic* modelling to predict the winner, the All Blacks will prevail by 26 points to 17 against their old rivals.
The QBE predictor put the odds of an All Blacks victory at 70%, significantly higher than the Wallabies at just 27%. There’s a 2% probability attached to a draw. According to QBE’s analysis, if the Wallabies are to cause an upset, the game will have to be extremely low scoring. As the table below shows, the more points scored, the more likely it is that the All Blacks will prevail.
While the model has had an 80% success rate in predicting the winner of each match in the 2015 tournament so far, Business Insider doesn’t believe that’s much of an achievement, given how obvious the results were in the pool stage (excluding you, Eddie Jones, and Japan). And it predicted the English team would have a far better tournament than being knocked out at pool stage (most models would predict English failure in world cups by now, surely?).
The reality is the pressure is on New Zealand and the Wallabies are exactly where they want to be as underdogs.
While the majority of pundits and the QBE model suggest the Wallabies can’t win, it’s clear that you cannot write off Michael Cheika’s team. They’ve proved doubters wrong over and over in this tournament against the English, Welsh, Scots and Argentina.
The All Blacks have a tendency to crack under pressure. Even in the last world cup final, on home turf at Eden Park in Auckland, they nearly coughed up what was expected to have been the easiest of victories against the French.
This time, on the other side of the world, without a bipartisan crowd cheering on every move, there are things you can’t factor into stochastic* modelling.
* Wikipedia defines as stochastic model as “a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time. The random variation is usually based on fluctuations observed in historical data for a selected period using standard time-series techniques”.
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