#334 California-Santa Barbara-B (8-14)

avg: 581.39  •  sd: 59.37  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
352 Cal Poly-SLO-C Win 9-8 623.92 Jan 20th Pres Day Quals
230 California-Davis Loss 5-13 415.39 Jan 20th Pres Day Quals
396 California-San Diego-C Win 8-7 242.11 Jan 20th Pres Day Quals
211 San Diego State Loss 8-11 714.53 Jan 20th Pres Day Quals
344 Chico State Win 12-5 1140.15 Mar 9th Silicon Valley Rally 2024
158 UCLA-B** Loss 5-13 689.07 Ignored Mar 9th Silicon Valley Rally 2024
354 California-Santa Cruz-B Win 11-8 856.93 Mar 9th Silicon Valley Rally 2024
221 California-B Loss 7-11 582.02 Mar 10th Silicon Valley Rally 2024
124 San Jose State** Loss 4-13 792.34 Ignored Mar 10th Silicon Valley Rally 2024
158 UCLA-B Loss 9-13 870.51 Mar 10th Silicon Valley Rally 2024
219 Arizona Loss 7-12 531.97 Mar 30th 2024 Sinvite
133 Arizona State** Loss 3-13 775.09 Ignored Mar 30th 2024 Sinvite
221 California-B Loss 6-9 630.35 Mar 30th 2024 Sinvite
235 Claremont Loss 3-15 395.21 Mar 30th 2024 Sinvite
332 California-San Diego-B Win 15-5 1191.08 Mar 31st 2024 Sinvite
298 Southern California-B Loss 10-15 266.54 Mar 31st 2024 Sinvite
396 California-San Diego-C Win 10-5 691 Apr 13th Southwest Dev Mens Conferences 2024
- Arizona -B Win 14-6 600 Ignored Apr 14th Southwest Dev Mens Conferences 2024
332 California-San Diego-B Loss 3-15 -8.92 Apr 14th Southwest Dev Mens Conferences 2024
396 California-San Diego-C Win 15-5 717.11 Apr 14th Southwest Dev Mens Conferences 2024
298 Southern California-B Loss 9-12 374.78 Apr 14th Southwest Dev Mens Conferences 2024
354 California-Santa Cruz-B Loss 8-9 366.33 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)