#104 California-San Diego-B (10-9)

avg: 1266.85  •  sd: 90.93  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
162 Arizona Win 8-7 979.59 Feb 1st Presidents Day Qualifiers 2025
194 Cal State-Long Beach** Win 10-2 1195.6 Ignored Feb 1st Presidents Day Qualifiers 2025
25 UCLA** Loss 0-13 1533.45 Ignored Feb 1st Presidents Day Qualifiers 2025
166 UCLA-B Win 8-3 1437.04 Feb 1st Presidents Day Qualifiers 2025
162 Arizona Win 9-4 1454.59 Feb 2nd Presidents Day Qualifiers 2025
63 California-Irvine Loss 2-11 1042.75 Feb 2nd Presidents Day Qualifiers 2025
1 British Columbia** Loss 0-13 2402.29 Ignored Feb 16th Presidents Day Invite 2025
11 Utah** Loss 0-13 1878.8 Ignored Feb 16th Presidents Day Invite 2025
25 UCLA** Loss 2-13 1533.45 Ignored Feb 16th Presidents Day Invite 2025
63 California-Irvine Loss 4-8 1077.94 Mar 2nd Claremont Classic 2025
166 UCLA-B Win 13-4 1437.04 Mar 2nd Claremont Classic 2025
126 Claremont-B Win 9-5 1643.08 Mar 2nd Claremont Classic 2025
122 Claremont Win 7-5 1478.93 Mar 2nd Claremont Classic 2025
89 San Diego State Loss 4-7 899.32 Mar 8th Gnomageddon
199 California-Santa Barbara-B** Win 10-3 1172.3 Ignored Mar 8th Gnomageddon
166 UCLA-B Loss 2-3 712.04 Mar 8th Gnomageddon
122 Claremont Win 8-3 1750.79 Mar 8th Gnomageddon
89 San Diego State Loss 5-7 1067.34 Mar 9th Gnomageddon
166 UCLA-B Win 5-3 1255.6 Mar 9th Gnomageddon
**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)