#137 North Carolina-B (10-11)

avg: 1233.15  •  sd: 60.99  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
91 Mary Washington Loss 11-13 1153.67 Feb 2nd Mid Atlantic Warmup 2019
113 Davidson Loss 12-13 1176.9 Feb 2nd Mid Atlantic Warmup 2019
39 Vermont Loss 7-13 1148.24 Feb 2nd Mid Atlantic Warmup 2019
166 Virginia Commonwealth Loss 7-13 534.3 Feb 2nd Mid Atlantic Warmup 2019
91 Mary Washington Loss 13-15 1168.33 Feb 3rd Mid Atlantic Warmup 2019
157 Drexel Win 15-11 1510.57 Feb 3rd Mid Atlantic Warmup 2019
197 George Mason Win 15-11 1382.56 Feb 3rd Mid Atlantic Warmup 2019
83 Rutgers Loss 8-12 991.82 Feb 23rd Oak Creek Challenge 2019
345 American** Win 10-3 1101.81 Ignored Feb 23rd Oak Creek Challenge 2019
114 Liberty Loss 10-11 1175.11 Feb 23rd Oak Creek Challenge 2019
338 Wake Forest** Win 13-0 1133.6 Ignored Feb 23rd Oak Creek Challenge 2019
158 Lehigh Loss 9-15 613.6 Feb 24th Oak Creek Challenge 2019
250 Maryland-Baltimore County Win 15-9 1370.74 Feb 24th Oak Creek Challenge 2019
166 Virginia Commonwealth Win 10-9 1216.83 Feb 24th Oak Creek Challenge 2019
139 Pennsylvania Win 13-9 1648.24 Mar 30th Atlantic Coast Open 2019
158 Lehigh Win 13-9 1547.64 Mar 30th Atlantic Coast Open 2019
102 Georgetown Win 11-8 1716.79 Mar 30th Atlantic Coast Open 2019
299 Towson Win 12-7 1203.16 Mar 30th Atlantic Coast Open 2019
66 Penn State Loss 12-15 1234.75 Mar 31st Atlantic Coast Open 2019
118 MIT Loss 11-13 1058.89 Mar 31st Atlantic Coast Open 2019
115 Villanova Loss 13-14 1171.39 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)