#34 William & Mary (23-5)

avg: 1648.2  •  sd: 48.57  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
44 Illinois Loss 8-11 1223.42 Feb 3rd Mid Atlantic Warmup 2018
145 Drexel Win 9-3 1749.31 Feb 3rd Mid Atlantic Warmup 2018
227 Syracuse Win 13-7 1393.79 Feb 3rd Mid Atlantic Warmup 2018
109 Williams Win 13-3 1896.21 Feb 3rd Mid Atlantic Warmup 2018
48 Dartmouth Loss 11-12 1440.43 Feb 4th Mid Atlantic Warmup 2018
194 George Washington** Win 15-6 1564.43 Ignored Feb 4th Mid Atlantic Warmup 2018
78 Georgetown Win 8-7 1540.07 Feb 4th Mid Atlantic Warmup 2018
124 Indiana Loss 9-13 807.7 Feb 17th Easterns Qualifier 2018
64 North Carolina-Charlotte Win 10-8 1725.17 Feb 17th Easterns Qualifier 2018
113 Lehigh Win 13-7 1841.61 Feb 17th Easterns Qualifier 2018
102 Richmond Win 13-7 1884.41 Feb 17th Easterns Qualifier 2018
224 Georgia Southern** Win 13-5 1461.24 Ignored Feb 17th Easterns Qualifier 2018
124 Indiana Win 11-9 1475.47 Feb 18th Easterns Qualifier 2018
12 North Carolina State Loss 10-12 1680.74 Feb 18th Easterns Qualifier 2018
66 Kennesaw State Win 14-9 1931.88 Feb 18th Easterns Qualifier 2018
169 Johns Hopkins Win 13-5 1660.68 Mar 17th Oak Creek Invite 2018
119 Bates Win 12-11 1389.63 Mar 17th Oak Creek Invite 2018
134 Princeton Win 13-11 1403.73 Mar 17th Oak Creek Invite 2018
91 Penn State Win 12-9 1719.27 Mar 17th Oak Creek Invite 2018
61 James Madison Win 15-14 1597.52 Mar 18th Oak Creek Invite 2018
60 Cornell Win 14-13 1598.23 Mar 18th Oak Creek Invite 2018
33 Maryland Win 13-10 2012.43 Mar 18th Oak Creek Invite 2018
117 Pennsylvania Win 12-8 1712.47 Mar 24th Atlantic Coast Open 2018
167 North Carolina-B Win 12-7 1586.76 Mar 24th Atlantic Coast Open 2018
22 Tufts Loss 11-12 1625.18 Mar 24th Atlantic Coast Open 2018
61 James Madison Win 15-6 2072.52 Mar 25th Atlantic Coast Open 2018
78 Georgetown Win 13-10 1743.21 Mar 25th Atlantic Coast Open 2018
22 Tufts Win 10-9 1875.18 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)