#144 Santa Clara (10-12)

avg: 1336.2  •  sd: 49.06  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
332 California-San Diego-B** Win 13-5 1191.08 Ignored Jan 20th Pres Day Quals
158 UCLA-B Loss 9-12 943.71 Jan 20th Pres Day Quals
121 Cal Poly-SLO-B Loss 8-9 1276.25 Jan 21st Pres Day Quals
230 California-Davis Win 9-4 1615.39 Feb 3rd Stanford Open 2024
344 Chico State** Win 13-3 1140.15 Ignored Feb 3rd Stanford Open 2024
192 Loyola Marymount Win 9-4 1755.9 Feb 3rd Stanford Open 2024
60 California-Santa Barbara Loss 6-13 1115.6 Mar 2nd Stanford Invite 2024
33 California-Santa Cruz Loss 7-11 1440.29 Mar 2nd Stanford Invite 2024
111 Vanderbilt Loss 9-11 1207.96 Mar 2nd Stanford Invite 2024
121 Cal Poly-SLO-B Loss 10-13 1073.11 Mar 3rd Stanford Invite 2024
113 Southern California Loss 9-12 1108.67 Mar 3rd Stanford Invite 2024
338 Cal Poly-Humboldt Win 13-6 1163.56 Apr 13th NorCal D I Mens Conferences 2024
33 California-Santa Cruz Loss 4-15 1307.18 Apr 13th NorCal D I Mens Conferences 2024
344 Chico State** Win 14-5 1140.15 Ignored Apr 13th NorCal D I Mens Conferences 2024
124 San Jose State Loss 8-9 1267.34 Apr 13th NorCal D I Mens Conferences 2024
230 California-Davis Win 15-4 1615.39 Apr 14th NorCal D I Mens Conferences 2024
328 Nevada-Reno Win 14-7 1184.89 Apr 14th NorCal D I Mens Conferences 2024
133 Arizona State Win 7-6 1500.09 Apr 27th Southwest D I College Mens Regionals 2024
15 California Loss 7-15 1557.46 Apr 27th Southwest D I College Mens Regionals 2024
34 California-San Diego Loss 4-14 1303.45 Apr 27th Southwest D I College Mens Regionals 2024
71 Grand Canyon Win 12-11 1754.57 Apr 27th Southwest D I College Mens Regionals 2024
67 Stanford Loss 5-11 1071.57 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)