#87 Case Western Reserve (12-9)

avg: 1422.56  •  sd: 62.22  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
120 James Madison Win 13-7 1840.33 Feb 2nd Mid Atlantic Warmup 2019
32 William & Mary Loss 7-13 1189.15 Feb 2nd Mid Atlantic Warmup 2019
88 Tennessee-Chattanooga Win 13-11 1648.03 Feb 2nd Mid Atlantic Warmup 2019
157 Drexel Win 11-6 1676.1 Feb 2nd Mid Atlantic Warmup 2019
39 Vermont Loss 11-14 1392.43 Feb 3rd Mid Atlantic Warmup 2019
151 SUNY-Binghamton Win 15-6 1762.14 Feb 3rd Mid Atlantic Warmup 2019
113 Davidson Win 15-10 1755.5 Feb 3rd Mid Atlantic Warmup 2019
126 New Hampshire Win 13-11 1504.26 Feb 16th Easterns Qualifier 2019
155 Elon Win 10-5 1723.48 Feb 16th Easterns Qualifier 2019
61 Tennessee Loss 8-13 1058.03 Feb 16th Easterns Qualifier 2019
102 Georgetown Loss 7-8 1226.18 Feb 16th Easterns Qualifier 2019
139 Pennsylvania Loss 8-11 864.06 Feb 17th Easterns Qualifier 2019
101 Connecticut Win 15-10 1809.84 Feb 17th Easterns Qualifier 2019
88 Tennessee-Chattanooga Loss 8-11 1053.58 Feb 17th Easterns Qualifier 2019
53 Indiana Loss 11-13 1397.78 Mar 23rd CWRUL Memorial 2019
171 RIT Win 13-10 1409.79 Mar 23rd CWRUL Memorial 2019
132 Kentucky Win 9-8 1376.16 Mar 23rd CWRUL Memorial 2019
53 Indiana Loss 9-15 1111.14 Mar 24th CWRUL Memorial 2019
38 Purdue Loss 8-15 1142.23 Mar 24th CWRUL Memorial 2019
174 Cedarville Win 13-9 1486.02 Mar 24th CWRUL Memorial 2019
135 University of Pittsburgh-B Win 14-9 1716.9 Mar 24th CWRUL Memorial 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)