#120 James Madison (13-14)

avg: 1282.8  •  sd: 61.1  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
113 Davidson Loss 10-12 1063.77 Feb 2nd Mid Atlantic Warmup 2019
114 Liberty Win 13-5 1900.11 Feb 2nd Mid Atlantic Warmup 2019
110 Williams Loss 10-12 1077.7 Feb 2nd Mid Atlantic Warmup 2019
87 Case Western Reserve Loss 7-13 865.03 Feb 2nd Mid Atlantic Warmup 2019
158 Lehigh Win 13-12 1254.08 Feb 3rd Mid Atlantic Warmup 2019
157 Drexel Win 15-9 1644.89 Feb 3rd Mid Atlantic Warmup 2019
91 Mary Washington Win 15-12 1683 Feb 3rd Mid Atlantic Warmup 2019
139 Pennsylvania Win 10-8 1492.34 Feb 16th Easterns Qualifier 2019
38 Purdue Loss 7-13 1149.51 Feb 16th Easterns Qualifier 2019
165 Georgia Southern Win 13-8 1588.07 Feb 16th Easterns Qualifier 2019
39 Vermont Loss 9-11 1456.56 Feb 16th Easterns Qualifier 2019
126 New Hampshire Loss 11-12 1150.42 Feb 17th Easterns Qualifier 2019
88 Tennessee-Chattanooga Loss 9-14 945.32 Feb 17th Easterns Qualifier 2019
101 Connecticut Win 14-9 1830.11 Feb 17th Easterns Qualifier 2019
32 William & Mary Loss 11-15 1365.52 Feb 23rd Virginia Showcase Series 22319
147 Delaware Win 10-7 1577.61 Mar 16th Oak Creek Invite 2019
18 Michigan Loss 6-13 1308.77 Mar 16th Oak Creek Invite 2019
142 Princeton Win 11-10 1334.71 Mar 16th Oak Creek Invite 2019
163 SUNY-Geneseo Loss 7-10 716.91 Mar 16th Oak Creek Invite 2019
108 North Carolina-Charlotte Loss 5-15 725.07 Mar 17th Oak Creek Invite 2019
157 Drexel Win 13-12 1254.41 Mar 17th Oak Creek Invite 2019
83 Rutgers Win 11-9 1682.18 Mar 30th Atlantic Coast Open 2019
33 Johns Hopkins Loss 7-13 1173.63 Mar 30th Atlantic Coast Open 2019
101 Connecticut Loss 6-10 860.08 Mar 30th Atlantic Coast Open 2019
115 Villanova Loss 10-12 1058.27 Mar 30th Atlantic Coast Open 2019
195 George Washington Win 10-8 1266.48 Mar 31st Atlantic Coast Open 2019
102 Georgetown Win 12-11 1476.18 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)