#232 George Washington (6-12)

avg: 924.71  •  sd: 67.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
181 American Loss 7-8 1013.96 Feb 22nd Monument Melee 2025
173 East Carolina Loss 4-11 566.37 Feb 22nd Monument Melee 2025
332 Villanova Win 11-9 818.79 Feb 22nd Monument Melee 2025
155 Johns Hopkins Loss 7-10 848.85 Feb 23rd Monument Melee 2025
238 Virginia Commonwealth Win 10-8 1168.44 Feb 23rd Monument Melee 2025
300 Maryland-Baltimore County Win 8-3 1285.62 Feb 23rd Monument Melee 2025
115 RIT Loss 4-15 767 Mar 22nd Atlantic Coast Open 2025
96 Lehigh Loss 4-13 864.61 Mar 22nd Atlantic Coast Open 2025
239 Wake Forest Loss 6-9 486.2 Mar 22nd Atlantic Coast Open 2025
45 Virginia Tech** Loss 4-15 1152.57 Ignored Mar 22nd Atlantic Coast Open 2025
413 American-B** Win 15-1 285.62 Ignored Mar 23rd Atlantic Coast Open 2025
264 Virginia Tech-B Win 15-7 1406.34 Mar 23rd Atlantic Coast Open 2025
181 American Loss 8-9 1013.96 Apr 12th Colonial D I Mens Conferences 2025
162 Delaware Loss 6-8 912.79 Apr 12th Colonial D I Mens Conferences 2025
64 Georgetown** Loss 2-15 1028.4 Ignored Apr 12th Colonial D I Mens Conferences 2025
300 Maryland-Baltimore County Win 12-5 1285.62 Apr 12th Colonial D I Mens Conferences 2025
181 American Loss 11-14 825.62 Apr 13th Colonial D I Mens Conferences 2025
162 Delaware Loss 8-15 648.47 Apr 13th Colonial D I Mens Conferences 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)