#43 Portland (15-4)

avg: 1661.6  •  sd: 68.87  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
97 Stanford-B Win 12-1 1835.17 Feb 3rd Stanford Open 2024
30 Cal Poly-SLO Loss 8-9 1716.91 Feb 3rd Stanford Open 2024
137 California-B** Win 13-2 1547.01 Ignored Feb 3rd Stanford Open 2024
37 Carleton College-Eclipse Win 8-5 2203.01 Feb 10th DIII Grand Prix
48 Colorado College Win 9-6 2031.93 Feb 10th DIII Grand Prix
154 Oregon State Win 11-5 1459.31 Feb 10th DIII Grand Prix
118 Puget Sound Win 8-4 1674.86 Feb 10th DIII Grand Prix
75 Lewis & Clark Win 9-4 1975.08 Feb 11th DIII Grand Prix
46 Whitman Loss 6-8 1339.82 Feb 11th DIII Grand Prix
46 Whitman Loss 3-7 1040.31 Feb 11th DIII Grand Prix
154 Oregon State** Win 13-2 1459.31 Ignored Feb 24th PLU Womens BBQ 2024
189 Pacific Lutheran** Win 13-0 1166.31 Ignored Feb 24th PLU Womens BBQ 2024
118 Puget Sound Win 12-8 1551.21 Feb 24th PLU Womens BBQ 2024
118 Puget Sound Win 9-4 1710.05 Feb 25th PLU Womens BBQ 2024
75 Lewis & Clark Win 10-9 1500.08 Apr 13th Northwest D III Womens Conferences 2024
189 Pacific Lutheran** Win 15-4 1166.31 Ignored Apr 13th Northwest D III Womens Conferences 2024
118 Puget Sound Win 13-6 1710.05 Apr 13th Northwest D III Womens Conferences 2024
46 Whitman Loss 10-11 1515.31 Apr 13th Northwest D III Womens Conferences 2024
46 Whitman Win 11-10 1765.31 Apr 14th Northwest D III Womens Conferences 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
What's the algorithm again?
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
Is there a longer explanation of all this?
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)