May 9, 2017

Super 18 Predictions for Round 12

Team Ratings for Round 12

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
Hurricanes 17.39 13.22 4.20
Crusaders 13.87 8.75 5.10
Highlanders 9.17 9.17 0.00
Chiefs 8.54 9.75 -1.20
Lions 8.36 7.64 0.70
Blues 3.47 -1.07 4.50
Brumbies 2.02 3.83 -1.80
Sharks 1.40 0.42 1.00
Stormers 0.63 1.51 -0.90
Waratahs -0.91 5.81 -6.70
Jaguares -2.84 -4.36 1.50
Bulls -4.48 0.29 -4.80
Force -8.96 -9.45 0.50
Cheetahs -9.47 -7.36 -2.10
Reds -10.60 -10.28 -0.30
Kings -13.37 -19.02 5.70
Rebels -14.52 -8.17 -6.40
Sunwolves -16.80 -17.76 1.00

 

Performance So Far

So far there have been 87 matches played, 67 of which were correctly predicted, a success rate of 77%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Hurricanes vs. Stormers May 05 41 – 22 21.00 TRUE
2 Cheetahs vs. Highlanders May 05 41 – 45 -16.10 TRUE
3 Rebels vs. Lions May 06 10 – 47 -16.40 TRUE
4 Chiefs vs. Reds May 06 46 – 17 22.30 TRUE
5 Waratahs vs. Blues May 06 33 – 40 0.50 FALSE
6 Sharks vs. Force May 06 37 – 12 12.90 TRUE
7 Bulls vs. Crusaders May 06 24 – 62 -11.10 TRUE
8 Jaguares vs. Sunwolves May 06 46 – 39 19.50 TRUE

 

Predictions for Round 12

Here are the predictions for Round 12. 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 Blues vs. Cheetahs May 12 Blues 16.90
2 Brumbies vs. Lions May 12 Lions -2.30
3 Crusaders vs. Hurricanes May 13 Hurricanes -3.50
4 Rebels vs. Reds May 13 Reds -0.40
5 Bulls vs. Highlanders May 13 Highlanders -9.70
6 Kings vs. Sharks May 13 Sharks -11.30
7 Jaguares vs. Force May 13 Jaguares 10.10

 

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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
    Brent Ranken

    How about predicting the EPL footbsll league,
    cheers

    2 weeks ago Reply

    • avatar

      I would need to use a different method. The scores in football (soccer) are mainly small integer values, whereas the method I use has an implicit assumption of a near continuous distribution for the score differences. The score differences are of course still integers but because the number of different values that the score differences take is reasonably large, the violation of the continuous distribution assumption is not so great that the method fails.

      There are statisticians predicting the EPL. One such is http://www.pickforwin.com. I went to a talk by one of the developers of the method used on that site.

      2 weeks ago Reply

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