#221 California-B (14-9)

avg: 1048.92  •  sd: 61.01  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
192 Loyola Marymount Loss 4-11 555.9 Jan 20th Pres Day Quals
396 California-San Diego-C Win 13-7 674.64 Jan 20th Pres Day Quals
396 California-San Diego-C** Win 12-5 717.11 Ignored Jan 21st Pres Day Quals
332 California-San Diego-B Win 11-6 1137.77 Jan 21st Pres Day Quals
338 Cal Poly-Humboldt Win 11-6 1110.26 Feb 3rd Stanford Open 2024
121 Cal Poly-SLO-B Loss 9-11 1152.04 Feb 3rd Stanford Open 2024
339 Occidental Win 10-5 1132.31 Feb 3rd Stanford Open 2024
338 Cal Poly-Humboldt Win 13-4 1163.56 Mar 9th Silicon Valley Rally 2024
230 California-Davis Win 9-8 1140.39 Mar 9th Silicon Valley Rally 2024
124 San Jose State Loss 8-10 1129.67 Mar 9th Silicon Valley Rally 2024
158 UCLA-B Loss 5-13 689.07 Mar 10th Silicon Valley Rally 2024
334 California-Santa Barbara-B Win 11-7 1048.28 Mar 10th Silicon Valley Rally 2024
124 San Jose State Win 9-6 1810.9 Mar 10th Silicon Valley Rally 2024
219 Arizona Loss 8-9 927.48 Mar 30th 2024 Sinvite
133 Arizona State Loss 4-13 775.09 Mar 30th 2024 Sinvite
235 Claremont Win 9-6 1413.77 Mar 30th 2024 Sinvite
334 California-Santa Barbara-B Win 9-6 999.96 Mar 30th 2024 Sinvite
219 Arizona Win 7-4 1548.64 Mar 31st 2024 Sinvite
124 San Jose State Loss 8-12 951.18 Mar 31st 2024 Sinvite
121 Cal Poly-SLO-B Loss 5-14 801.25 Apr 14th Southwest Dev Mens Conferences 2024
332 California-San Diego-B Win 11-5 1191.08 Apr 14th Southwest Dev Mens Conferences 2024
298 Southern California-B Win 12-8 1161.3 Apr 14th Southwest Dev Mens Conferences 2024
158 UCLA-B Loss 6-15 689.07 Apr 14th Southwest Dev Mens Conferences 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)