#14 UCLA (15-8)

avg: 1830.83  •  sd: 57.45  •  top 16/20: 93.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
3 Brigham Young Loss 11-15 1736.35 Jan 28th Santa Barbara Invitational 2023
47 Case Western Reserve Win 13-12 1580.38 Jan 28th Santa Barbara Invitational 2023
33 Victoria Win 11-10 1751.01 Jan 28th Santa Barbara Invitational 2023
110 Southern California Win 13-8 1627.72 Jan 28th Santa Barbara Invitational 2023
16 British Columbia Win 8-7 1902.02 Jan 29th Santa Barbara Invitational 2023
26 California Loss 10-12 1454.99 Jan 29th Santa Barbara Invitational 2023
9 California-Santa Cruz Loss 10-15 1441.86 Jan 29th Santa Barbara Invitational 2023
30 Utah State Win 12-10 1888.82 Feb 18th President’s Day Invite
45 Western Washington Win 12-6 2068.57 Feb 18th President’s Day Invite
26 California Win 10-3 2293.11 Feb 18th President’s Day Invite
52 Colorado State Win 14-5 2036.47 Feb 19th President’s Day Invite
7 Oregon Loss 9-10 1843.08 Feb 19th President’s Day Invite
43 Grand Canyon Loss 7-11 1045.01 Feb 19th President’s Day Invite
64 California-San Diego Win 12-8 1820.75 Feb 19th President’s Day Invite
31 Oregon State Win 12-8 2072.18 Feb 20th President’s Day Invite
9 California-Santa Cruz Win 10-8 2158.13 Feb 20th President’s Day Invite
10 Minnesota Loss 11-12 1763.56 Mar 4th Smoky Mountain Invite
11 Pittsburgh Win 11-9 2116.92 Mar 4th Smoky Mountain Invite
27 Northeastern Win 12-9 2027.82 Mar 4th Smoky Mountain Invite
15 North Carolina State Win 11-10 1930.28 Mar 4th Smoky Mountain Invite
12 Carleton College Win 13-12 1988.49 Mar 5th Smoky Mountain Invite
4 Texas Loss 10-15 1567.9 Mar 5th Smoky Mountain Invite
5 Vermont Loss 13-15 1796.53 Mar 5th Smoky Mountain Invite
**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)