#6 Oregon (16-8)

avg: 2219.89  •  sd: 64.81  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
15 California-Santa Cruz Win 13-11 2272.61 Jan 25th Santa Barbara Invite 2025
4 Colorado Loss 11-12 2202.97 Jan 25th Santa Barbara Invite 2025
20 Pennsylvania Win 13-6 2512.4 Jan 25th Santa Barbara Invite 2025
1 British Columbia Loss 8-11 2199.59 Jan 26th Santa Barbara Invite 2025
14 California-Santa Barbara Win 11-6 2616.69 Jan 26th Santa Barbara Invite 2025
2 Carleton College Win 10-8 2826.19 Jan 26th Santa Barbara Invite 2025
41 Southern California** Win 13-2 2173.6 Ignored Feb 15th Presidents Day Invite 2025
16 Victoria Win 11-9 2235.11 Feb 15th Presidents Day Invite 2025
27 Texas Win 13-7 2328.2 Feb 16th Presidents Day Invite 2025
16 Victoria Win 10-6 2482.06 Feb 16th Presidents Day Invite 2025
18 Western Washington Win 11-8 2332.23 Feb 16th Presidents Day Invite 2025
1 British Columbia Loss 10-13 2237.06 Feb 17th Presidents Day Invite 2025
10 California-San Diego Win 13-9 2532.28 Feb 17th Presidents Day Invite 2025
4 Colorado Win 13-12 2452.97 Feb 17th Presidents Day Invite 2025
11 Michigan Loss 11-12 1985.35 Mar 22nd Northwest Challenge 2025
18 Western Washington Win 15-11 2347.79 Mar 22nd Northwest Challenge 2025
8 Stanford Win 13-8 2630.17 Mar 22nd Northwest Challenge 2025
1 British Columbia Loss 7-13 2007.67 Mar 23rd Northwest Challenge 2025
2 Carleton College Loss 11-13 2334.68 Mar 23rd Northwest Challenge 2025
9 North Carolina Win 10-7 2507.93 Mar 23rd Northwest Challenge 2025
52 Oregon State Win 14-11 1777.43 Apr 3rd Cascadia D I Womens Conferences 2025
63 British Columbia-B** Win 15-2 1931.04 Ignored Apr 12th Cascadia D I Womens Conferences 2025
7 Washington Loss 7-15 1589.3 Apr 12th Cascadia D I Womens Conferences 2025
18 Western Washington Loss 10-15 1513.02 Apr 12th Cascadia D I Womens 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)