#197 George Mason (8-18)

avg: 1001.39  •  sd: 57.86  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
85 Richmond Loss 10-12 1191.58 Feb 2nd Mid Atlantic Warmup 2019
158 Lehigh Win 13-12 1254.08 Feb 2nd Mid Atlantic Warmup 2019
151 SUNY-Binghamton Loss 6-13 562.14 Feb 2nd Mid Atlantic Warmup 2019
195 George Washington Win 13-7 1561.34 Feb 2nd Mid Atlantic Warmup 2019
137 North Carolina-B Loss 11-15 851.99 Feb 3rd Mid Atlantic Warmup 2019
195 George Washington Win 15-12 1304.3 Feb 3rd Mid Atlantic Warmup 2019
166 Virginia Commonwealth Win 15-12 1392.32 Feb 3rd Mid Atlantic Warmup 2019
53 Indiana** Loss 5-13 1026.62 Ignored Feb 16th Easterns Qualifier 2019
119 Clemson Loss 10-12 1045.43 Feb 16th Easterns Qualifier 2019
64 Ohio Loss 5-13 939.4 Feb 16th Easterns Qualifier 2019
81 Georgia Tech Loss 10-13 1119.17 Feb 16th Easterns Qualifier 2019
155 Elon Loss 12-13 1024.58 Feb 17th Easterns Qualifier 2019
145 Dayton Loss 11-15 808.51 Feb 17th Easterns Qualifier 2019
165 Georgia Southern Loss 10-15 638.3 Feb 17th Easterns Qualifier 2019
47 Maryland** Loss 1-13 1056.33 Ignored Mar 16th Oak Creek Invite 2019
150 Cornell Loss 6-13 578.08 Mar 16th Oak Creek Invite 2019
157 Drexel Loss 11-13 900.57 Mar 16th Oak Creek Invite 2019
73 Temple Loss 6-13 880.87 Mar 16th Oak Creek Invite 2019
188 East Carolina Loss 11-14 717.03 Mar 17th Oak Creek Invite 2019
204 SUNY-Buffalo Win 15-3 1571.8 Mar 17th Oak Creek Invite 2019
171 RIT Win 11-9 1330.86 Mar 30th Atlantic Coast Open 2019
242 Rowan Loss 9-13 467.89 Mar 30th Atlantic Coast Open 2019
345 American Win 14-3 1101.81 Mar 30th Atlantic Coast Open 2019
115 Villanova Loss 11-13 1067.55 Mar 30th Atlantic Coast Open 2019
187 NYU Loss 9-12 685.24 Mar 31st Atlantic Coast Open 2019
299 Towson Win 14-5 1282.65 Mar 31st Atlantic Coast Open 2019
**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)