#45 California-Irvine (9-4)

avg: 1048.33  •  sd: 118.99  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
74 Arizona State Win 11-3 1150.62 Feb 1st Presidents Day Qualifiers 2025
9 Cal Poly-SLO** Loss 2-13 1092.49 Ignored Feb 1st Presidents Day Qualifiers 2025
43 California Loss 6-7 937.06 Feb 1st Presidents Day Qualifiers 2025
106 California-San Diego-C** Win 13-1 -335.05 Ignored Feb 1st Presidents Day Qualifiers 2025
9 Cal Poly-SLO** Loss 4-10 1092.49 Ignored Feb 2nd Presidents Day Qualifiers 2025
43 California Loss 5-8 608.46 Feb 2nd Presidents Day Qualifiers 2025
77 California-San Diego-B Win 11-2 1100.22 Feb 2nd Presidents Day Qualifiers 2025
76 Cal Poly-SLO-B Win 13-2 1118.91 Feb 15th Santa Clara University WLT Tournament
89 California-Davis-B** Win 13-2 882.77 Ignored Feb 15th Santa Clara University WLT Tournament
75 Occidental Win 13-3 1137.05 Feb 15th Santa Clara University WLT Tournament
96 Cal Poly-Humboldt** Win 13-1 591.84 Ignored Feb 16th Santa Clara University WLT Tournament
76 Cal Poly-SLO-B Win 13-2 1118.91 Feb 16th Santa Clara University WLT Tournament
54 Santa Clara Win 10-9 1098.55 Feb 16th Santa Clara University WLT Tournament
**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)