#30 UCLA (21-23)

avg: 1718.34  •  sd: 55.58  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
19 Brigham Young Loss 6-11 1383.74 Jan 25th Santa Barbara Invite 2025
2 Carleton College** Loss 5-13 1963.52 Ignored Jan 25th Santa Barbara Invite 2025
12 Utah Loss 5-11 1493.58 Jan 25th Santa Barbara Invite 2025
15 California-Santa Cruz Loss 8-9 1918.77 Jan 26th Santa Barbara Invite 2025
7 Washington Loss 6-7 2064.3 Jan 26th Santa Barbara Invite 2025
180 Arizona** Win 12-2 1137.6 Ignored Feb 1st Presidents Day Qualifiers 2025
220 Cal State-Long Beach** Win 13-0 822.63 Ignored Feb 1st Presidents Day Qualifiers 2025
123 California-San Diego-B** Win 13-0 1515.74 Ignored Feb 1st Presidents Day Qualifiers 2025
196 UCLA-B** Win 13-2 1068.07 Ignored Feb 1st Presidents Day Qualifiers 2025
13 Cal Poly-SLO Loss 5-8 1626.27 Feb 2nd Presidents Day Qualifiers 2025
40 California Win 11-5 2181.88 Feb 2nd Presidents Day Qualifiers 2025
19 Brigham Young Loss 8-12 1489.28 Feb 15th Presidents Day Invite 2025
4 Colorado Loss 8-13 1831.81 Feb 15th Presidents Day Invite 2025
1 British Columbia** Loss 4-13 1965.2 Ignored Feb 16th Presidents Day Invite 2025
123 California-San Diego-B** Win 13-2 1515.74 Ignored Feb 16th Presidents Day Invite 2025
12 Utah Loss 8-10 1830.91 Feb 16th Presidents Day Invite 2025
14 California-Santa Barbara Win 9-6 2488.56 Feb 17th Presidents Day Invite 2025
8 Stanford Loss 4-13 1534.01 Feb 17th Presidents Day Invite 2025
19 Brigham Young Loss 6-13 1330.43 Mar 1st Stanford Invite 2025 Womens
14 California-Santa Barbara Loss 11-12 1944.99 Mar 1st Stanford Invite 2025 Womens
45 Texas-Dallas Win 12-8 2002.8 Mar 1st Stanford Invite 2025 Womens
7 Washington Loss 5-13 1589.3 Mar 1st Stanford Invite 2025 Womens
40 California Win 9-6 2000.44 Mar 2nd Stanford Invite 2025 Womens
23 California-Davis Loss 3-8 1281.12 Mar 2nd Stanford Invite 2025 Womens
148 Boston University** Win 13-2 1323.49 Ignored Mar 22nd Womens Centex 2025
73 Colorado College Win 10-4 1868.82 Mar 22nd Womens Centex 2025
255 Texas-B** Win 13-0 466.15 Ignored Mar 22nd Womens Centex 2025
67 Illinois Win 15-6 1909.59 Mar 23rd Womens Centex 2025
27 Texas Win 12-9 2116.03 Mar 23rd Womens Centex 2025
12 Utah Loss 12-15 1793.09 Mar 23rd Womens Centex 2025
83 California-Irvine Win 15-1 1794.75 Apr 12th SoCal D I Womens Conferences 2025
10 California-San Diego Loss 6-12 1534.4 Apr 12th SoCal D I Womens Conferences 2025
106 San Diego State** Win 15-5 1639.72 Ignored Apr 12th SoCal D I Womens Conferences 2025
41 Southern California Loss 9-10 1448.6 Apr 12th SoCal D I Womens Conferences 2025
83 California-Irvine Win 15-5 1794.75 Apr 13th SoCal D I Womens Conferences 2025
10 California-San Diego Loss 9-14 1639.85 Apr 13th SoCal D I Womens Conferences 2025
106 San Diego State** Win 15-4 1639.72 Ignored Apr 13th SoCal D I Womens Conferences 2025
10 California-San Diego Loss 10-15 1660.11 Apr 26th Southwest D I College Womens Regionals 2025
15 California-Santa Cruz Loss 9-15 1528.29 Apr 26th Southwest D I College Womens Regionals 2025
41 Southern California Win 14-12 1794.56 Apr 26th Southwest D I College Womens Regionals 2025
8 Stanford Loss 6-15 1534.01 Apr 26th Southwest D I College Womens Regionals 2025
40 California Win 13-12 1706.88 Apr 27th Southwest D I College Womens Regionals 2025
68 Santa Clara Win 15-7 1900.07 Apr 27th Southwest D I College Womens Regionals 2025
41 Southern California Loss 12-15 1273.11 Apr 27th Southwest D I College Womens Regionals 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)