August 17, 2016

Mitre 10 Cup Predictions for Round 1

Team Ratings for Round 1

The basic method is described on my Department home page.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Current Rating Rating at Season Start Difference
Canterbury 12.85 12.85 0.00
Auckland 11.34 11.34 0.00
Tasman 8.71 8.71 0.00
Taranaki 8.25 8.25 0.00
Wellington 4.32 4.32 0.00
Counties Manukau 2.45 2.45 0.00
Hawke’s Bay 1.85 1.85 0.00
Otago 0.54 0.54 0.00
Waikato -4.31 -4.31 0.00
Bay of Plenty -5.54 -5.54 0.00
Manawatu -6.71 -6.71 0.00
North Harbour -8.15 -8.15 0.00
Southland -9.71 -9.71 0.00
Northland -19.37 -19.37 0.00

 

Predictions for Round 1

Here are the predictions for Round 1. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Game Date Winner Prediction
1 North Harbour vs. Counties Manukau Aug 18 Counties Manukau -6.60
2 Northland vs. Manawatu Aug 19 Manawatu -8.70
3 Bay of Plenty vs. Taranaki Aug 20 Taranaki -9.80
4 Hawke’s Bay vs. Wellington Aug 20 Hawke’s Bay 1.50
5 Canterbury vs. Auckland Aug 20 Canterbury 5.50
6 Southland vs. Otago Aug 21 Otago -6.20
7 Tasman vs. Waikato Aug 21 Tasman 17.00

 

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David Scott obtained a BA and PhD from the Australian National University and then commenced his university teaching career at La Trobe University in 1972. He has taught at La Trobe University, the University of Sheffield, Bond University and Colorado State University, joining the University of Auckland, based at Tamaki Campus, in mid-1995. He has been Head of Department at La Trobe University, Acting Dean and Associate Dean (Academic) at Bond University, and Associate Director of the Centre for Quality Management and Data Analysis at Bond University with responsibility for Short Courses. He was Head of the Department of Statistics in 2000, and is a past President of the New Zealand Statistical Assocation. See all posts by David Scott »

Comments

  • avatar

    Very interesting.

    We are currently doing local live stats on these same matches. I wonder if you would have any interest in such data for analysis?

    From a brief look, you method is feedback based? I have quite an extensive historical database, perhaps it would be interesting to talk.

    8 years ago