#127 Santa Clara (9-13)

avg: 1341.61  •  sd: 45.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
216 Cal Poly-Pomona Win 13-7 1573.09 Feb 1st Pres Day Quals men
299 California-Santa Barbara-B** Win 12-5 1287.76 Ignored Feb 1st Pres Day Quals men
84 Southern California Loss 5-13 919.35 Feb 1st Pres Day Quals men
46 Stanford Loss 5-13 1165.36 Feb 2nd Pres Day Quals men
211 UCLA-B Win 11-7 1492.19 Feb 2nd Pres Day Quals men
10 British Columbia** Loss 2-13 1551.93 Ignored Mar 8th Stanford Invite 2025 Mens
13 California Loss 6-13 1503.63 Mar 8th Stanford Invite 2025 Mens
121 Northwestern Win 10-9 1488.35 Mar 8th Stanford Invite 2025 Mens
46 Stanford Loss 6-13 1165.36 Mar 8th Stanford Invite 2025 Mens
43 Whitman Loss 6-13 1183.61 Mar 9th Stanford Invite 2025 Mens
84 Southern California Loss 7-12 998.84 Mar 9th Stanford Invite 2025 Mens
13 California** Loss 4-15 1503.63 Ignored Apr 12th NorCal D I Mens Conferences 2025
276 Chico State Win 13-7 1341.64 Apr 12th NorCal D I Mens Conferences 2025
261 Nevada-Reno Win 13-7 1389.17 Apr 12th NorCal D I Mens Conferences 2025
124 San Jose State Win 12-10 1597.06 Apr 12th NorCal D I Mens Conferences 2025
9 California-Santa Cruz** Loss 3-15 1585.14 Ignored Apr 13th NorCal D I Mens Conferences 2025
46 Stanford Loss 7-15 1165.36 Apr 13th NorCal D I Mens Conferences 2025
120 Arizona State Loss 11-13 1137.08 Apr 26th Southwest D I College Mens Regionals 2025
41 California-San Diego Loss 6-15 1192.31 Apr 26th Southwest D I College Mens Regionals 2025
124 San Jose State Win 12-11 1483.94 Apr 26th Southwest D I College Mens Regionals 2025
54 UCLA Loss 11-14 1390.74 Apr 26th Southwest D I College Mens Regionals 2025
173 California-Davis Win 15-8 1747.17 Apr 27th Southwest D I College Mens Regionals 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)