#147 George Washington (13-7)

avg: 881.09  •  sd: 80.15  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
155 Appalachian State Win 12-7 1370.58 Feb 2nd Hucking Shucking 2019
209 North Carolina-B Win 13-6 1086.44 Feb 2nd Hucking Shucking 2019
192 William & Mary-B Win 13-7 1134.54 Feb 2nd Hucking Shucking 2019
199 Miami Win 10-7 943.29 Feb 3rd Hucking Shucking 2019
288 University of North Carolina - Asheville** Win 13-1 600 Ignored Feb 3rd Hucking Shucking 2019
117 Catholic Win 13-6 1643.8 Feb 3rd Hucking Shucking 2019
117 Catholic Loss 10-12 805.67 Feb 3rd Hucking Shucking 2019
258 Maryland-Baltimore County** Win 13-2 675.92 Ignored Feb 16th Cherry Blossom Classic 2019
242 West Virginia** Win 13-3 816.41 Ignored Feb 16th Cherry Blossom Classic 2019
166 Richmond Win 8-6 1070.97 Feb 16th Cherry Blossom Classic 2019
94 Carnegie Mellon Loss 8-10 922.05 Feb 16th Cherry Blossom Classic 2019
145 American Loss 6-11 345.08 Feb 17th Cherry Blossom Classic 2019
258 Maryland-Baltimore County** Win 10-4 675.92 Ignored Feb 17th Cherry Blossom Classic 2019
94 Carnegie Mellon Loss 5-13 584.72 Feb 17th Cherry Blossom Classic 2019
71 William & Mary Loss 4-13 726.62 Mar 30th Atlantic Coast Open 2019
157 Virginia Commonwealth Loss 6-10 345.74 Mar 30th Atlantic Coast Open 2019
182 George Mason Win 13-4 1228.31 Mar 30th Atlantic Coast Open 2019
197 Christopher Newport Win 13-4 1165.13 Mar 30th Atlantic Coast Open 2019
182 George Mason Win 9-7 907.65 Mar 31st Atlantic Coast Open 2019
197 Christopher Newport Loss 6-9 146.56 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)