#175 California-Santa Cruz-B (15-7)

avg: 1180.4  •  sd: 61.97  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
119 Cal Poly-SLO-B Loss 8-10 1104.54 Feb 1st Pres Day Quals men
136 California-Irvine Loss 8-13 824.06 Feb 1st Pres Day Quals men
376 San Diego State-B** Win 13-2 866.51 Ignored Feb 1st Pres Day Quals men
216 Cal Poly-Pomona Win 12-4 1615.56 Feb 2nd Pres Day Quals men
374 Cal Poly-SLO-C Win 13-6 888.4 Feb 2nd Pres Day Quals men
211 UCLA-B Win 12-4 1625.29 Feb 8th Stanford Open Mens
237 Loyola Marymount Win 12-10 1163.5 Feb 8th Stanford Open Mens
200 Cal Poly-Humboldt Loss 8-9 939.82 Feb 9th Stanford Open Mens
124 San Jose State Loss 9-10 1233.94 Feb 9th Stanford Open Mens
250 Portland Win 12-8 1324.89 Feb 9th Stanford Open Mens
200 Cal Poly-Humboldt Win 11-8 1430.43 Mar 15th Silicon Valley Rally 2025
279 California-B Win 10-5 1351.2 Mar 15th Silicon Valley Rally 2025
276 Chico State Win 13-3 1384.1 Mar 15th Silicon Valley Rally 2025
124 San Jose State Loss 8-9 1233.94 Mar 15th Silicon Valley Rally 2025
279 California-B Win 10-5 1351.2 Mar 16th Silicon Valley Rally 2025
124 San Jose State Win 8-7 1483.94 Mar 16th Silicon Valley Rally 2025
316 Arizona State-B Win 13-6 1218.09 Apr 12th Southwest Dev Mens Conferences 2025
350 California-San Diego-B Win 13-8 932.44 Apr 12th Southwest Dev Mens Conferences 2025
285 Southern California-B Win 13-3 1353.46 Apr 12th Southwest Dev Mens Conferences 2025
211 UCLA-B Win 11-8 1390.9 Apr 12th Southwest Dev Mens Conferences 2025
119 Cal Poly-SLO-B Loss 9-15 851.72 Apr 13th Southwest Dev Mens Conferences 2025
211 UCLA-B Loss 7-15 425.29 Apr 13th Southwest Dev Mens Conferences 2025
**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)