NRL Predictions for Round 6
Team Ratings for Round 6
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 | |
|---|---|---|---|
| Storm | 10.44 | 9.29 | 1.10 |
| Panthers | 6.60 | 8.50 | -1.90 |
| Sharks | 4.68 | 5.10 | -0.40 |
| Roosters | 3.85 | 7.44 | -3.60 |
| Sea Eagles | 3.18 | 2.97 | 0.20 |
| Bulldogs | 2.70 | 0.07 | 2.60 |
| Cowboys | 2.24 | 4.11 | -1.90 |
| Broncos | 0.75 | -1.82 | 2.60 |
| Warriors | -1.24 | -1.68 | 0.40 |
| Knights | -1.55 | -0.05 | -1.50 |
| Dolphins | -2.57 | -1.96 | -0.60 |
| Raiders | -2.71 | -3.61 | 0.90 |
| Rabbitohs | -2.75 | -4.35 | 1.60 |
| Dragons | -4.30 | -4.55 | 0.30 |
| Titans | -4.82 | -5.50 | 0.70 |
| Eels | -5.82 | -3.02 | -2.80 |
| Wests Tigers | -8.70 | -10.97 | 2.30 |
Performance So Far
So far there have been 40 matches played, 22 of which were correctly predicted, a success rate of 55%.
Here are the predictions for last week’s games.
| Game | Date | Score | Prediction | Correct | |
|---|---|---|---|---|---|
| 1 | Raiders vs. Sharks | Apr 03 | 24 – 20 | -5.30 | FALSE |
| 2 | Panthers vs. Cowboys | Apr 04 | 18 – 22 | 8.60 | FALSE |
| 3 | Rabbitohs vs. Roosters | Apr 04 | 20 – 14 | -4.70 | FALSE |
| 4 | Eels vs. Dragons | Apr 05 | 23 – 22 | 1.60 | TRUE |
| 5 | Titans vs. Dolphins | Apr 05 | 10 – 36 | 3.10 | FALSE |
| 6 | Broncos vs. Wests Tigers | Apr 05 | 46 – 24 | 11.40 | TRUE |
| 7 | Sea Eagles vs. Storm | Apr 06 | 24 – 48 | -2.40 | TRUE |
| 8 | Bulldogs vs. Knights | Apr 06 | 20 – 0 | 5.90 | TRUE |
Predictions for Round 6
Here are the predictions for Round 6. 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 | Dolphins vs. Panthers | Apr 10 | Panthers | -6.20 |
| 2 | Dragons vs. Titans | Apr 11 | Dragons | 3.50 |
| 3 | Broncos vs. Roosters | Apr 11 | Roosters | -0.10 |
| 4 | Sharks vs. Sea Eagles | Apr 12 | Sharks | 4.50 |
| 5 | Rabbitohs vs. Cowboys | Apr 12 | Cowboys | -2.00 |
| 6 | Eels vs. Raiders | Apr 12 | Raiders | -3.10 |
| 7 | Storm vs. Warriors | Apr 13 | Storm | 15.20 |
| 8 | Knights vs. Wests Tigers | Apr 13 | Knights | 10.10 |
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
Add a comment
First time commenting? Please use your real first name and surname and read the Comment Policy.
2025 nrl round 6 Can you predict and give me the score figures for nrl round 6 which is coming this weekend
3 weeks ago
Hi David,
Have you noticed or are you aware of a trend in this model changing over the course of a season? ie. is it typical for the model to sit at ~60% accuracy by Round 6 of a competition, but it to increase (or decrease) by later rounds?
To frame the question differently, does the model tend to stand up better within a given season as it accumulates more current/relevant data, as opposed to being based on longer-term data from previous seasons?
2 weeks ago
I haven’t actually checked by tracing the accuracy over a season, but I suspect it to be the case. I have often noticed substantial form changes from one season to the next. The Panthers form is an obvious example this year, but also the Roosters have different form to last year so far. The algorithm can take some time to adjust to large changes in form.
I do have a parameter which I estimate which endeavours to reduce the season to season dependence, but it’s effect is typically quite small. Essentially large form changes from year to year can be expected, but for which teams and in what direction is not something statistics can help with.
2 weeks ago