#85 Southern California (14-9)

avg: 1317.15  •  sd: 71.82  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
191 Cal Poly-Pomona Win 13-5 1455.68 Feb 1st Pres Day Quals men
280 California-Santa Barbara-B Win 13-6 1064.59 Feb 1st Pres Day Quals men
140 Santa Clara Win 13-5 1676.39 Feb 1st Pres Day Quals men
114 California-Irvine Win 10-9 1322.3 Feb 2nd Pres Day Quals men
58 Grand Canyon Loss 5-13 900.66 Feb 2nd Pres Day Quals men
181 Arizona Win 11-5 1504.94 Feb 15th Vice Presidents Day Invite 2025
129 Arizona State Win 13-3 1724.63 Feb 15th Vice Presidents Day Invite 2025
191 Cal Poly-Pomona Win 11-10 980.68 Feb 15th Vice Presidents Day Invite 2025
111 San Jose State Win 10-9 1331.71 Feb 15th Vice Presidents Day Invite 2025
114 California-Irvine Win 12-4 1797.3 Feb 16th Vice Presidents Day Invite 2025
58 Grand Canyon Loss 11-12 1375.66 Feb 16th Vice Presidents Day Invite 2025
41 California-San Diego Loss 9-12 1276.1 Mar 8th Stanford Invite 2025 Mens
9 California-Santa Cruz Loss 6-13 1421.18 Mar 8th Stanford Invite 2025 Mens
43 Virginia Tech Loss 11-12 1487.2 Mar 8th Stanford Invite 2025 Mens
55 UCLA Win 13-8 2031.4 Mar 8th Stanford Invite 2025 Mens
33 California-Santa Barbara Loss 10-13 1332.79 Mar 9th Stanford Invite 2025 Mens
140 Santa Clara Win 12-7 1596.9 Mar 9th Stanford Invite 2025 Mens
304 Cal State-Long Beach** Win 13-2 962.79 Ignored Mar 29th Southwest Showdown 2025
95 Claremont Win 13-10 1603.79 Mar 29th Southwest Showdown 2025
186 Occidental Win 13-10 1204.06 Mar 29th Southwest Showdown 2025
95 Claremont Loss 3-13 675.64 Mar 30th Southwest Showdown 2025
106 San Diego State Loss 8-13 733.92 Mar 30th Southwest Showdown 2025
111 San Jose State Loss 10-13 878.57 Mar 30th Southwest Showdown 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)