#90 San Diego State (14-4)

avg: 959.3  •  sd: 61.97  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
140 Brigham Young-B Win 13-8 1028.7 Jan 25th New Year Fest 2025
65 Colorado-B Loss 11-12 1026.77 Jan 25th New Year Fest 2025
186 Denver-B** Win 13-2 390.09 Ignored Jan 25th New Year Fest 2025
166 Grand Canyon-B Win 13-6 857.51 Jan 25th New Year Fest 2025
65 Colorado-B Loss 8-13 655.61 Jan 26th New Year Fest 2025
111 Denver Win 11-7 1280.34 Jan 26th New Year Fest 2025
155 Northern Arizona Win 13-8 852.72 Jan 26th New Year Fest 2025
151 Chico State Win 13-3 1007.01 Feb 8th Stanford Open Mens
108 Washington-B Win 8-7 961.09 Feb 8th Stanford Open Mens
114 Cal Poly-Humboldt Win 9-5 1291.14 Feb 9th Stanford Open Mens
84 Cal Poly-SLO-B Loss 7-12 504.14 Feb 9th Stanford Open Mens
102 San Jose State Win 10-7 1253.5 Feb 9th Stanford Open Mens
129 Arizona Win 10-9 754.02 Feb 15th Vice Presidents Day Invite 2025
130 Cal Poly-Pomona Win 12-11 749.98 Feb 15th Vice Presidents Day Invite 2025
94 California-Irvine Win 10-8 1193.72 Feb 15th Vice Presidents Day Invite 2025
157 Loyola Marymount Win 12-9 701.74 Feb 15th Vice Presidents Day Invite 2025
94 California-Irvine Win 9-7 1210.39 Feb 16th Vice Presidents Day Invite 2025
55 Grand Canyon Loss 7-8 1091.94 Feb 16th Vice Presidents Day Invite 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)