#86 San Diego State University (12-5)

avg: 1174.11  •  sd: 78.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
120 Denver Loss 6-7 803.84 Jan 25th New Year Fest 2020
183 Arizona** Win 12-2 1045.82 Ignored Jan 25th New Year Fest 2020
148 Arizona State Win 8-7 858.56 Jan 25th New Year Fest 2020
256 Arizona-B** Win 13-0 600 Ignored Jan 25th New Year Fest 2020
54 New Mexico Loss 5-9 867.95 Jan 26th New Year Fest 2020
130 Northern Arizona Win 11-4 1461.82 Jan 26th New Year Fest 2020
157 Humboldt State Win 13-1 1219.64 Feb 8th Stanford Open 2020
175 Lewis & Clark** Win 13-0 1098.88 Ignored Feb 8th Stanford Open 2020
58 California-Santa Cruz Loss 4-13 766.65 Feb 8th Stanford Open 2020
67 Carleton College Win 11-0 1899.76 Feb 9th Stanford Open 2020
76 Portland Loss 6-7 1127.55 Feb 9th Stanford Open 2020
140 Santa Clara Win 9-3 1402.09 Mar 7th Santa Clara Rage Home Tournament 2020
196 California-B Win 10-5 875.96 Mar 7th Santa Clara Rage Home Tournament 2020
124 California-San Diego-B Win 8-5 1362.59 Mar 7th Santa Clara Rage Home Tournament 2020
144 Nevada-Reno Win 9-5 1305.52 Mar 7th Santa Clara Rage Home Tournament 2020
100 Cal State-Long Beach Loss 6-7 939.93 Mar 8th Santa Clara Rage Home Tournament 2020
124 California-San Diego-B Win 10-6 1405.14 Mar 8th Santa Clara Rage Home Tournament 2020
**Blowout Eligible

FAQ

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
  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
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)