#125 California-Irvine (8-14)

avg: 1391.92  •  sd: 49.07  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
121 Cal Poly-SLO-B Loss 8-12 960.1 Jan 20th Pres Day Quals
298 Southern California-B** Win 13-1 1320.15 Ignored Jan 20th Pres Day Quals
354 California-Santa Cruz-B** Win 13-4 1091.33 Ignored Jan 20th Pres Day Quals
230 California-Davis Win 10-7 1405.05 Jan 21st Pres Day Quals
5 Cal Poly-SLO Loss 8-15 1832.04 Feb 17th Presidents Day Invite 2024
22 Washington Loss 9-15 1518.01 Feb 17th Presidents Day Invite 2024
32 Utah State Loss 9-15 1393.42 Feb 17th Presidents Day Invite 2024
21 British Columbia Loss 8-15 1485.08 Feb 18th Presidents Day Invite 2024
60 California-Santa Barbara Loss 7-12 1195.08 Feb 18th Presidents Day Invite 2024
34 California-San Diego Loss 7-14 1320.56 Feb 18th Presidents Day Invite 2024
67 Stanford Loss 4-12 1071.57 Feb 19th Presidents Day Invite 2024
33 California-Santa Cruz Loss 5-13 1307.18 Feb 19th Presidents Day Invite 2024
5 Cal Poly-SLO** Loss 2-13 1796.85 Ignored Apr 13th SoCal D I Mens Conferences 2024
34 California-San Diego Loss 5-13 1303.45 Apr 13th SoCal D I Mens Conferences 2024
192 Loyola Marymount Win 10-7 1545.56 Apr 13th SoCal D I Mens Conferences 2024
255 Cal State-Long Beach Win 13-7 1480.32 Apr 14th SoCal D I Mens Conferences 2024
211 San Diego State Win 10-8 1342.8 Apr 14th SoCal D I Mens Conferences 2024
113 Southern California Win 10-8 1716.7 Apr 14th SoCal D I Mens Conferences 2024
60 California-Santa Barbara Loss 5-12 1115.6 Apr 27th Southwest D I College Mens Regionals 2024
158 UCLA-B Win 10-7 1678.74 Apr 27th Southwest D I College Mens Regionals 2024
34 California-San Diego Loss 8-13 1407.29 Apr 27th Southwest D I College Mens Regionals 2024
71 Grand Canyon Loss 8-12 1188.42 Apr 28th Southwest D I College Mens Regionals 2024
**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)