#68 Occidental (16-2)

avg: 1385.25  •  sd: 76.11  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
329 California-Irvine-B** Win 12-5 704.94 Ignored Feb 1st 2020 Mens Presidents Day Qualifier
214 California-San Diego-B Win 13-8 1276.5 Feb 1st 2020 Mens Presidents Day Qualifier
179 Grand Canyon Win 10-7 1314.39 Feb 1st 2020 Mens Presidents Day Qualifier
284 San Jose State** Win 12-5 1031.9 Ignored Feb 1st 2020 Mens Presidents Day Qualifier
181 Colorado-B Win 10-4 1516.83 Feb 2nd 2020 Mens Presidents Day Qualifier
203 Cal Poly-SLO-B Win 9-8 948.79 Feb 2nd 2020 Mens Presidents Day Qualifier
40 Santa Clara Loss 7-11 1145.02 Feb 2nd 2020 Mens Presidents Day Qualifier
148 Sonoma State Win 10-9 1132.81 Feb 2nd 2020 Mens Presidents Day Qualifier
86 Arizona State-B-B Loss 5-10 723.17 Feb 22nd Pomona Sweethearts 2020
279 Cal State-Fullerton** Win 13-3 1060.09 Ignored Feb 22nd Pomona Sweethearts 2020
159 California-Irvine Win 8-2 1591.54 Feb 22nd Pomona Sweethearts 2020
163 UCLA-B Win 9-4 1580.45 Feb 22nd Pomona Sweethearts 2020
187 Loyola Marymount Win 11-4 1494.41 Feb 22nd Pomona Sweethearts 2020
214 California-San Diego-B Win 11-5 1380.34 Feb 29th 2nd Annual Claremont Ultimate Classic
88 Claremont Win 13-8 1780.08 Feb 29th 2nd Annual Claremont Ultimate Classic
88 Claremont Win 9-6 1702.49 Feb 29th 2nd Annual Claremont Ultimate Classic
163 UCLA-B Win 10-3 1580.45 Feb 29th 2nd Annual Claremont Ultimate Classic
187 Loyola Marymount Win 9-4 1494.41 Feb 29th 2nd Annual Claremont Ultimate Classic
**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)