#194 George Washington (5-15)

avg: 964.43  •  sd: 76.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
193 Liberty Win 11-8 1332.11 Feb 3rd Mid Atlantic Warmup 2018
86 Duke Win 12-7 1919.49 Feb 3rd Mid Atlantic Warmup 2018
250 Maryland-Baltimore County Loss 9-11 519.49 Feb 3rd Mid Atlantic Warmup 2018
115 Villanova Loss 9-10 1151.67 Feb 3rd Mid Atlantic Warmup 2018
102 Richmond Loss 7-15 726.88 Feb 4th Mid Atlantic Warmup 2018
34 William & Mary** Loss 6-15 1048.2 Ignored Feb 4th Mid Atlantic Warmup 2018
115 Villanova Loss 7-13 719.14 Feb 4th Mid Atlantic Warmup 2018
145 Drexel Loss 6-13 549.31 Feb 24th Oak Creek Challenge 2018
107 Rutgers Loss 11-12 1189.37 Feb 24th Oak Creek Challenge 2018
318 Towson Win 12-11 635.45 Feb 24th Oak Creek Challenge 2018
56 Temple Loss 6-13 909.6 Feb 24th Oak Creek Challenge 2018
169 Johns Hopkins Loss 9-12 715.31 Feb 25th Oak Creek Challenge 2018
107 Rutgers Loss 11-12 1189.37 Feb 25th Oak Creek Challenge 2018
109 Williams Win 13-11 1525.05 Feb 25th Oak Creek Challenge 2018
48 Dartmouth Loss 6-13 965.43 Mar 24th Atlantic Coast Open 2018
139 Luther Loss 8-13 671.88 Mar 24th Atlantic Coast Open 2018
368 Edinboro** Win 13-1 911.46 Ignored Mar 24th Atlantic Coast Open 2018
56 Temple Loss 7-13 952.07 Mar 24th Atlantic Coast Open 2018
167 North Carolina-B Loss 8-11 700.64 Mar 25th Atlantic Coast Open 2018
84 Virginia Loss 11-13 1173.3 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)