#114 Liberty (13-7)

avg: 1300.11  •  sd: 83.3  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
120 James Madison Loss 5-13 682.8 Feb 2nd Mid Atlantic Warmup 2019
110 Williams Loss 12-13 1190.82 Feb 2nd Mid Atlantic Warmup 2019
113 Davidson Win 13-9 1720.46 Feb 2nd Mid Atlantic Warmup 2019
32 William & Mary Loss 7-13 1189.15 Feb 2nd Mid Atlantic Warmup 2019
158 Lehigh Loss 8-15 564.27 Feb 3rd Mid Atlantic Warmup 2019
91 Mary Washington Loss 9-15 867.03 Feb 3rd Mid Atlantic Warmup 2019
195 George Washington Loss 8-13 507.65 Feb 3rd Mid Atlantic Warmup 2019
83 Rutgers Win 10-9 1557.97 Feb 23rd Oak Creek Challenge 2019
345 American** Win 13-2 1101.81 Ignored Feb 23rd Oak Creek Challenge 2019
137 North Carolina-B Win 11-10 1358.15 Feb 23rd Oak Creek Challenge 2019
338 Wake Forest** Win 13-1 1133.6 Ignored Feb 23rd Oak Creek Challenge 2019
171 RIT Win 11-10 1206.65 Feb 24th Oak Creek Challenge 2019
84 Brandeis Win 9-8 1556.89 Feb 24th Oak Creek Challenge 2019
157 Drexel Win 15-10 1583.01 Feb 24th Oak Creek Challenge 2019
66 Penn State Loss 7-13 977.71 Mar 30th Atlantic Coast Open 2019
195 George Washington Win 13-5 1603.81 Mar 30th Atlantic Coast Open 2019
166 Virginia Commonwealth Win 12-9 1437.19 Mar 30th Atlantic Coast Open 2019
187 NYU Win 9-7 1309.94 Mar 30th Atlantic Coast Open 2019
171 RIT Win 13-5 1681.65 Mar 31st Atlantic Coast Open 2019
158 Lehigh Win 15-2 1729.08 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)