#48 Dartmouth (14-7)

avg: 1565.43  •  sd: 67.24  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
107 Rutgers Loss 10-12 1076.24 Feb 3rd Mid Atlantic Warmup 2018
78 Georgetown Win 12-9 1760.44 Feb 3rd Mid Atlantic Warmup 2018
126 Elon Win 13-6 1812.12 Feb 3rd Mid Atlantic Warmup 2018
84 Virginia Win 13-10 1730.28 Feb 3rd Mid Atlantic Warmup 2018
44 Illinois Loss 11-13 1360.19 Feb 4th Mid Atlantic Warmup 2018
34 William & Mary Win 12-11 1773.2 Feb 4th Mid Atlantic Warmup 2018
51 Ohio State Win 14-11 1851.03 Feb 4th Mid Atlantic Warmup 2018
61 James Madison Win 13-8 1968.68 Feb 17th Easterns Qualifier 2018
66 Kennesaw State Loss 7-10 1068.35 Feb 17th Easterns Qualifier 2018
149 Davidson Win 11-6 1687.55 Feb 17th Easterns Qualifier 2018
98 Clemson Win 11-9 1587.25 Feb 17th Easterns Qualifier 2018
151 George Mason Win 12-7 1637.35 Feb 17th Easterns Qualifier 2018
124 Indiana Loss 14-15 1101.26 Feb 18th Easterns Qualifier 2018
23 Georgia Tech Loss 8-15 1179.16 Feb 18th Easterns Qualifier 2018
66 Kennesaw State Win 15-6 2058.01 Feb 18th Easterns Qualifier 2018
61 James Madison Win 13-9 1891.09 Mar 24th Atlantic Coast Open 2018
28 Carnegie Mellon Win 10-9 1843.65 Mar 24th Atlantic Coast Open 2018
194 George Washington Win 13-6 1564.43 Mar 24th Atlantic Coast Open 2018
78 Georgetown Loss 10-11 1290.07 Mar 25th Atlantic Coast Open 2018
86 Duke Loss 10-12 1160.86 Mar 25th Atlantic Coast Open 2018
113 Lehigh Win 11-9 1533.29 Mar 25th Atlantic Coast Open 2018
**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)