#158 UCLA-B (13-6)

avg: 1289.07  •  sd: 81.97  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
332 California-San Diego-B Win 10-7 980.74 Jan 20th Pres Day Quals
144 Santa Clara Win 12-9 1681.56 Jan 20th Pres Day Quals
230 California-Davis Win 13-9 1433.95 Jan 21st Pres Day Quals
211 San Diego State Win 11-6 1626.83 Jan 21st Pres Day Quals
192 Loyola Marymount Loss 7-10 766.23 Jan 21st Pres Day Quals
334 California-Santa Barbara-B** Win 13-5 1181.39 Ignored Mar 9th Silicon Valley Rally 2024
354 California-Santa Cruz-B** Win 13-2 1091.33 Ignored Mar 9th Silicon Valley Rally 2024
344 Chico State Win 12-7 1060.66 Mar 9th Silicon Valley Rally 2024
221 California-B Win 13-5 1648.92 Mar 10th Silicon Valley Rally 2024
334 California-Santa Barbara-B Win 13-9 999.96 Mar 10th Silicon Valley Rally 2024
124 San Jose State Loss 6-13 792.34 Mar 10th Silicon Valley Rally 2024
- Arizona -B** Win 15-5 600 Ignored Apr 14th Southwest Dev Mens Conferences 2024
121 Cal Poly-SLO-B Loss 6-15 801.25 Apr 14th Southwest Dev Mens Conferences 2024
221 California-B Win 15-6 1648.92 Apr 14th Southwest Dev Mens Conferences 2024
298 Southern California-B Win 14-4 1320.15 Apr 14th Southwest Dev Mens Conferences 2024
125 California-Irvine Loss 7-10 1002.25 Apr 27th Southwest D I College Mens Regionals 2024
33 California-Santa Cruz Loss 9-12 1561.82 Apr 27th Southwest D I College Mens Regionals 2024
133 Arizona State Win 11-8 1740.7 Apr 28th Southwest D I College Mens Regionals 2024
113 Southern California Loss 8-9 1329.03 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)