#39 Vermont (15-4)

avg: 1705.77  •  sd: 60  •  top 16/20: 0.5%

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# Opponent Result Game Rating Status Date Event
91 Mary Washington Win 13-5 1982.51 Feb 2nd Mid Atlantic Warmup 2019
157 Drexel Win 13-4 1729.41 Feb 2nd Mid Atlantic Warmup 2019
137 North Carolina-B Win 13-7 1790.69 Feb 2nd Mid Atlantic Warmup 2019
166 Virginia Commonwealth** Win 13-4 1691.83 Ignored Feb 2nd Mid Atlantic Warmup 2019
85 Richmond Win 15-8 1994.51 Feb 3rd Mid Atlantic Warmup 2019
87 Case Western Reserve Win 14-11 1735.9 Feb 3rd Mid Atlantic Warmup 2019
32 William & Mary Win 15-14 1871.68 Feb 3rd Mid Atlantic Warmup 2019
120 James Madison Win 11-9 1532.01 Feb 16th Easterns Qualifier 2019
139 Pennsylvania Win 13-12 1354.67 Feb 16th Easterns Qualifier 2019
38 Purdue Win 13-7 2264.57 Feb 16th Easterns Qualifier 2019
165 Georgia Southern** Win 13-4 1691.91 Ignored Feb 16th Easterns Qualifier 2019
38 Purdue Win 15-14 1832.04 Feb 17th Easterns Qualifier 2019
102 Georgetown Win 15-7 1951.18 Feb 17th Easterns Qualifier 2019
44 Virginia Loss 10-15 1217.81 Feb 17th Easterns Qualifier 2019
23 Texas Tech Loss 5-11 1231.13 Mar 30th Huck Finn XXIII
31 Texas A&M Win 10-9 1873.41 Mar 30th Huck Finn XXIII
98 Kansas Win 7-3 1963.18 Mar 31st Huck Finn XXIII
18 Michigan Loss 6-9 1490.2 Mar 31st Huck Finn XXIII
31 Texas A&M Loss 7-9 1469.07 Mar 31st Huck Finn XXIII
**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)