#23 UCLA (9-15)

avg: 1959.3  •  sd: 79.16  •  top 16/20: 17.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
14 Brigham Young Loss 6-11 1663.94 Jan 25th Santa Barbara Invite 2025
3 Carleton College** Loss 5-13 2108.16 Ignored Jan 25th Santa Barbara Invite 2025
12 Utah Loss 5-11 1730.78 Jan 25th Santa Barbara Invite 2025
9 California-Santa Cruz Loss 8-9 2220.27 Jan 26th Santa Barbara Invite 2025
10 Washington Loss 6-7 2219.56 Jan 26th Santa Barbara Invite 2025
139 Arizona** Win 12-2 1359.35 Ignored Feb 1st Presidents Day Qualifiers 2025
176 Cal State-Long Beach** Win 13-0 925.28 Ignored Feb 1st Presidents Day Qualifiers 2025
83 California-San Diego-B** Win 13-0 1831.17 Ignored Feb 1st Presidents Day Qualifiers 2025
162 UCLA-B** Win 13-2 1134.3 Ignored Feb 1st Presidents Day Qualifiers 2025
13 Cal Poly-SLO Loss 5-8 1817.19 Feb 2nd Presidents Day Qualifiers 2025
37 California Win 11-5 2352.36 Feb 2nd Presidents Day Qualifiers 2025
14 Brigham Young Loss 8-12 1769.48 Feb 15th Presidents Day Invite 2025
5 Colorado Loss 8-13 1985.91 Feb 15th Presidents Day Invite 2025
1 British Columbia** Loss 4-13 2301.21 Ignored Feb 16th Presidents Day Invite 2025
83 California-San Diego-B** Win 13-2 1831.17 Ignored Feb 16th Presidents Day Invite 2025
12 Utah Loss 8-10 2068.11 Feb 16th Presidents Day Invite 2025
15 California-Santa Barbara Win 9-6 2579.89 Feb 17th Presidents Day Invite 2025
6 Stanford Loss 4-13 1773.21 Feb 17th Presidents Day Invite 2025
14 Brigham Young Loss 6-13 1610.64 Mar 1st Stanford Invite 2025 Womens
15 California-Santa Barbara Loss 11-12 2036.32 Mar 1st Stanford Invite 2025 Womens
46 Texas-Dallas Win 12-8 2124.76 Mar 1st Stanford Invite 2025 Womens
10 Washington Loss 5-13 1744.56 Mar 1st Stanford Invite 2025 Womens
37 California Win 9-6 2170.93 Mar 2nd Stanford Invite 2025 Womens
17 California-Davis Loss 3-8 1555.33 Mar 2nd Stanford Invite 2025 Womens
**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)