#250 Portland (8-12)

avg: 883.73  •  sd: 57.48  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
104 British Columbia -B Loss 4-12 851.5 Feb 8th Stanford Open Mens
255 Santa Clara-B Win 10-9 986.57 Feb 8th Stanford Open Mens
340 Stanford-B Win 10-8 795.62 Feb 8th Stanford Open Mens
119 Cal Poly-SLO-B Loss 6-13 767.2 Feb 9th Stanford Open Mens
175 California-Santa Cruz-B Loss 8-12 739.24 Feb 9th Stanford Open Mens
261 Nevada-Reno Loss 7-9 552.3 Feb 9th Stanford Open Mens
315 Pacific Lutheran Win 13-12 744.3 Mar 1st PLU BBQ men
291 Reed Loss 5-13 131.28 Mar 1st PLU BBQ men
378 Portland State Win 13-6 858.56 Mar 1st PLU BBQ men
138 Gonzaga Loss 10-15 859.6 Mar 2nd PLU BBQ men
251 Seattle Win 15-10 1331.58 Mar 2nd PLU BBQ men
291 Reed Win 15-11 1112.44 Mar 2nd PLU BBQ men
104 British Columbia -B Loss 3-15 851.5 Mar 29th Northwest Challenge D3
83 Simon Fraser** Loss 6-15 942.33 Ignored Mar 29th Northwest Challenge D3
139 Puget Sound Loss 8-15 740.5 Mar 30th Northwest Challenge D3
157 Washington-B Loss 10-12 1002.08 Mar 30th Northwest Challenge D3
40 Lewis & Clark** Loss 6-15 1207.59 Ignored Apr 19th Northwest D III Mens Conferences 2025
315 Pacific Lutheran Win 14-5 1219.3 Apr 19th Northwest D III Mens Conferences 2025
291 Reed Win 14-10 1129.98 Apr 19th Northwest D III Mens Conferences 2025
139 Puget Sound Loss 10-15 851.71 Apr 20th Northwest D III Mens Conferences 2025
**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)