#86 Arizona State-B-B (14-5)

avg: 1297.07  •  sd: 88.25  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
318 Arizona-B** Win 13-4 816 Ignored Jan 25th New Year Fest 2020
102 Northern Arizona Win 13-5 1806.85 Jan 25th New Year Fest 2020
282 Colorado Mesa University** Win 13-5 1044.84 Ignored Jan 25th New Year Fest 2020
165 New Mexico Win 12-7 1493.49 Jan 25th New Year Fest 2020
94 Denver Loss 8-9 1130.85 Jan 26th New Year Fest 2020
102 Northern Arizona Loss 7-8 1081.85 Jan 26th New Year Fest 2020
200 California-Santa Barbara-B Win 11-3 1434.01 Feb 1st 2020 Mens Presidents Day Qualifier
40 Santa Clara Loss 3-11 1011.92 Feb 1st 2020 Mens Presidents Day Qualifier
185 Cal State-Long Beach Loss 9-10 775.75 Feb 1st 2020 Mens Presidents Day Qualifier
163 UCLA-B Win 9-7 1259.79 Feb 1st 2020 Mens Presidents Day Qualifier
179 Grand Canyon Loss 3-9 324.73 Feb 2nd 2020 Mens Presidents Day Qualifier
189 Cal Poly-Pomona Win 10-2 1479.26 Feb 2nd 2020 Mens Presidents Day Qualifier
126 Chico State Win 12-6 1701.74 Feb 2nd 2020 Mens Presidents Day Qualifier
159 California-Irvine Win 8-5 1445.15 Feb 2nd 2020 Mens Presidents Day Qualifier
187 Loyola Marymount Win 10-7 1284.08 Feb 22nd Pomona Sweethearts 2020
68 Occidental Win 10-5 1959.14 Feb 22nd Pomona Sweethearts 2020
189 Cal Poly-Pomona Win 9-6 1297.83 Feb 22nd Pomona Sweethearts 2020
163 UCLA-B Win 8-5 1434.06 Feb 22nd Pomona Sweethearts 2020
303 Long Beach State University-B Win 7-3 911.2 Feb 22nd Pomona Sweethearts 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)