#29 UCLA (9-18)

avg: 1664.62  •  sd: 54.12  •  top 16/20: 3.2%

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# Opponent Result Game Rating Status Date Event
53 Cal Poly-SLO Loss 9-10 1255.02 Jan 28th Santa Barbara Invitational 2023
12 California-Santa Barbara Loss 4-14 1458.42 Jan 28th Santa Barbara Invitational 2023
74 Utah Win 12-8 1665.55 Jan 28th Santa Barbara Invitational 2023
15 Victoria Loss 1-15 1252.64 Jan 28th Santa Barbara Invitational 2023
50 California-Santa Cruz Loss 5-9 908.59 Jan 29th Santa Barbara Invitational 2023
42 Wisconsin Win 11-10 1631.49 Jan 29th Santa Barbara Invitational 2023
8 Stanford Loss 5-11 1633.33 Feb 18th President’s Day Invite
24 Carleton College-Eclipse Loss 7-9 1453.49 Feb 18th President’s Day Invite
18 Colorado State Loss 9-10 1686.5 Feb 18th President’s Day Invite
11 Oregon Loss 7-14 1513.93 Feb 18th President’s Day Invite
31 California Win 10-6 2154.12 Feb 19th President’s Day Invite
17 California-San Diego Loss 8-11 1458.97 Feb 19th President’s Day Invite
11 Oregon Loss 7-10 1707.15 Feb 19th President’s Day Invite
48 Texas Loss 9-10 1334.95 Feb 20th President’s Day Invite
74 Utah Win 10-6 1720.56 Feb 20th President’s Day Invite
2 British Columbia** Loss 1-13 1948.3 Ignored Mar 11th Stanford Invite Womens
25 California-Davis Win 6-5 1844.95 Mar 11th Stanford Invite Womens
11 Oregon Loss 8-12 1655.66 Mar 11th Stanford Invite Womens
4 Tufts** Loss 3-11 1826.44 Ignored Mar 11th Stanford Invite Womens
39 Santa Clara Win 9-7 1818.69 Mar 12th Stanford Invite Womens
25 California-Davis Win 8-4 2284.75 Mar 12th Stanford Invite Womens
20 Western Washington Loss 8-9 1655.43 Mar 12th Stanford Invite Womens
8 Stanford Loss 8-13 1737.17 Mar 25th Northwest Challenge1
7 Carleton College Loss 8-13 1782.19 Mar 25th Northwest Challenge1
9 Washington Loss 7-13 1625.99 Mar 25th Northwest Challenge1
48 Texas Win 13-7 2017.48 Mar 26th Northwest Challenge1
74 Utah Win 13-6 1824.4 Mar 26th Northwest Challenge1
**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)