#16 Victoria (9-16)

avg: 1985.9  •  sd: 63.23  •  top 16/20: 89.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
19 Brigham Young Loss 8-11 1564.82 Jan 25th Santa Barbara Invite 2025
14 California-Santa Barbara Loss 8-11 1704.38 Jan 25th Santa Barbara Invite 2025
8 Stanford Loss 9-11 1884.81 Jan 25th Santa Barbara Invite 2025
23 California-Davis Loss 5-6 1756.12 Jan 26th Santa Barbara Invite 2025
20 Pennsylvania Win 7-6 2037.4 Jan 26th Santa Barbara Invite 2025
19 Brigham Young Loss 7-9 1651.09 Feb 15th Presidents Day Invite 2025
14 California-Santa Barbara Win 10-6 2566.15 Feb 15th Presidents Day Invite 2025
6 Oregon Loss 9-11 1970.68 Feb 15th Presidents Day Invite 2025
6 Oregon Loss 6-10 1723.73 Feb 16th Presidents Day Invite 2025
27 Texas Win 8-5 2224.27 Feb 16th Presidents Day Invite 2025
18 Western Washington Win 13-12 2091.62 Feb 16th Presidents Day Invite 2025
23 California-Davis Loss 7-9 1601.79 Feb 17th Presidents Day Invite 2025
10 California-San Diego Loss 8-13 1617.56 Feb 17th Presidents Day Invite 2025
4 Colorado Loss 8-13 1831.81 Feb 17th Presidents Day Invite 2025
19 Brigham Young Win 13-7 2487.96 Mar 22nd Northwest Challenge 2025
4 Colorado Loss 8-12 1886.81 Mar 22nd Northwest Challenge 2025
9 North Carolina Loss 7-9 1838.93 Mar 22nd Northwest Challenge 2025
13 Cal Poly-SLO Win 12-7 2600.38 Mar 23rd Northwest Challenge 2025
10 California-San Diego Win 10-7 2503.38 Mar 23rd Northwest Challenge 2025
5 Vermont Loss 6-11 1739.59 Mar 23rd Northwest Challenge 2025
1 British Columbia Loss 10-15 2111.59 Apr 12th Cascadia D I Womens Conferences 2025
52 Oregon State Win 15-6 2064.09 Apr 12th Cascadia D I Womens Conferences 2025
18 Western Washington Win 14-11 2279.96 Apr 12th Cascadia D I Womens Conferences 2025
7 Washington Loss 10-14 1790.6 Apr 13th Cascadia D I Womens Conferences 2025
18 Western Washington Loss 8-11 1601.01 Apr 13th 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)