#138 California-Irvine (8-10)

avg: 1300.29  •  sd: 66.74  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
120 Cal Poly-SLO-B Loss 8-12 906.91 Feb 1st Pres Day Quals men
174 California-Santa Cruz-B Win 13-8 1659.14 Feb 1st Pres Day Quals men
379 San Diego State-B** Win 13-2 850.94 Ignored Feb 1st Pres Day Quals men
175 California-Davis Win 13-8 1658.81 Feb 2nd Pres Day Quals men
85 Southern California Loss 9-10 1374.33 Feb 2nd Pres Day Quals men
126 San Jose State Win 10-7 1729.86 Feb 15th Vice Presidents Day Invite 2025
65 Grand Canyon Loss 7-9 1345 Feb 15th Vice Presidents Day Invite 2025
109 San Diego State Loss 8-10 1148.13 Feb 15th Vice Presidents Day Invite 2025
217 Cal Poly-Pomona Win 13-2 1596.29 Feb 16th Vice Presidents Day Invite 2025
109 San Diego State Loss 7-9 1131.46 Feb 16th Vice Presidents Day Invite 2025
85 Southern California Loss 4-12 899.33 Feb 16th Vice Presidents Day Invite 2025
6 Cal Poly-SLO Loss 6-13 1636.91 Apr 12th SoCal D I Mens Conferences 2025
333 Cal State-Long Beach** Win 13-2 1165.67 Ignored Apr 12th SoCal D I Mens Conferences 2025
109 San Diego State Loss 8-12 969.64 Apr 12th SoCal D I Mens Conferences 2025
41 California-San Diego Loss 4-13 1172.27 Apr 12th SoCal D I Mens Conferences 2025
217 Cal Poly-Pomona Win 15-12 1296.78 Apr 13th SoCal D I Mens Conferences 2025
237 Loyola Marymount Win 15-9 1422.72 Apr 13th SoCal D I Mens Conferences 2025
109 San Diego State Loss 8-11 1045.18 Apr 13th SoCal D I Mens Conferences 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)