#89 San Diego State (13-4)

avg: 1395.48  •  sd: 74.34  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
224 Arizona-B** Win 13-4 946 Ignored Jan 25th New Year Fest 2025
175 Colorado-B Win 9-6 1198.19 Jan 25th New Year Fest 2025
98 Denver Win 10-9 1448.68 Jan 25th New Year Fest 2025
130 Northern Arizona Win 10-8 1351.71 Jan 25th New Year Fest 2025
85 Grand Canyon Win 9-6 1849.9 Jan 26th New Year Fest 2025
130 Northern Arizona Win 11-7 1555.94 Jan 26th New Year Fest 2025
104 California-San Diego-B Win 7-4 1763.01 Mar 8th Gnomageddon
199 California-Santa Barbara-B** Win 9-2 1172.3 Ignored Mar 8th Gnomageddon
166 UCLA-B Win 9-4 1437.04 Mar 8th Gnomageddon
63 California-Irvine Win 6-5 1767.75 Mar 9th Gnomageddon
104 California-San Diego-B Win 7-5 1594.99 Mar 9th Gnomageddon
220 California-San Diego-C Win 8-4 949.28 Mar 9th Gnomageddon
39 California Loss 10-15 1413.49 Mar 22nd Womens Centex 2025
83 Illinois Loss 6-13 839.05 Mar 22nd Womens Centex 2025
11 Utah** Loss 4-13 1878.8 Ignored Mar 22nd Womens Centex 2025
147 Boston University Win 14-11 1293.33 Mar 23rd Womens Centex 2025
72 Colorado College Loss 8-9 1431.22 Mar 23rd Womens Centex 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)