#111 San Jose State (15-7)

avg: 1206.71  •  sd: 47.48  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
175 Cal Poly-Humboldt Win 13-3 1536.36 Feb 8th Stanford Open Mens
262 California-B Win 11-7 1025.76 Feb 8th Stanford Open Mens
118 British Columbia -B Loss 5-7 857.87 Feb 9th Stanford Open Mens
137 California-Santa Cruz-B Win 10-9 1219.14 Feb 9th Stanford Open Mens
106 San Diego State Loss 7-10 840.41 Feb 9th Stanford Open Mens
114 California-Irvine Loss 7-10 807.63 Feb 15th Vice Presidents Day Invite 2025
58 Grand Canyon Loss 7-12 980.14 Feb 15th Vice Presidents Day Invite 2025
85 Southern California Loss 9-10 1192.15 Feb 15th Vice Presidents Day Invite 2025
181 Arizona Win 11-7 1371.83 Feb 16th Vice Presidents Day Invite 2025
129 Arizona State Win 9-8 1249.63 Feb 16th Vice Presidents Day Invite 2025
175 Cal Poly-Humboldt Win 11-7 1403.25 Mar 15th Silicon Valley Rally 2025
262 California-B Win 10-6 1055.02 Mar 15th Silicon Valley Rally 2025
137 California-Santa Cruz-B Win 9-8 1219.14 Mar 15th Silicon Valley Rally 2025
266 Chico State Win 12-7 1068.02 Mar 15th Silicon Valley Rally 2025
175 Cal Poly-Humboldt Win 10-7 1326.02 Mar 16th Silicon Valley Rally 2025
137 California-Santa Cruz-B Loss 7-8 969.14 Mar 16th Silicon Valley Rally 2025
280 California-Santa Barbara-B** Win 13-2 1064.59 Ignored Mar 29th Southwest Showdown 2025
106 San Diego State Win 9-8 1355.08 Mar 29th Southwest Showdown 2025
208 UCLA-B Win 9-6 1183.86 Mar 29th Southwest Showdown 2025
95 Claremont Loss 10-11 1150.64 Mar 30th Southwest Showdown 2025
106 San Diego State Win 13-11 1458.92 Mar 30th Southwest Showdown 2025
85 Southern California Win 13-10 1645.3 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)