#174 Cedarville (14-7)

avg: 1067.46  •  sd: 53.09  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
301 Salisbury Win 13-1 1253.13 Feb 23rd Oak Creek Challenge 2019
292 Navy Win 12-10 941.03 Feb 23rd Oak Creek Challenge 2019
84 Brandeis Loss 6-13 831.89 Feb 23rd Oak Creek Challenge 2019
158 Lehigh Win 13-8 1625.24 Feb 23rd Oak Creek Challenge 2019
171 RIT Loss 8-12 640.5 Feb 24th Oak Creek Challenge 2019
83 Rutgers Loss 11-15 1051.81 Feb 24th Oak Creek Challenge 2019
157 Drexel Win 13-12 1254.41 Feb 24th Oak Creek Challenge 2019
148 Michigan-B Loss 10-13 853.8 Mar 23rd CWRUL Memorial 2019
380 Case Western Reserve-B** Win 13-1 930.75 Ignored Mar 23rd CWRUL Memorial 2019
348 Western Michigan Win 11-8 853.18 Mar 23rd CWRUL Memorial 2019
320 Ohio State-B Win 13-3 1192.54 Mar 23rd CWRUL Memorial 2019
158 Lehigh Loss 8-15 564.27 Mar 24th CWRUL Memorial 2019
87 Case Western Reserve Loss 9-13 1003.99 Mar 24th CWRUL Memorial 2019
247 Xavier Win 13-12 999.74 Mar 24th CWRUL Memorial 2019
145 Dayton Loss 9-10 1064.68 Mar 24th CWRUL Memorial 2019
389 Cornell-B** Win 13-2 874.9 Ignored Mar 30th I 85 Rodeo 2019
349 William & Mary-B Win 13-0 1085.45 Mar 30th I 85 Rodeo 2019
318 Virginia Tech-B Win 13-4 1196.5 Mar 30th I 85 Rodeo 2019
334 James Madison-B Win 15-5 1147.25 Mar 31st I 85 Rodeo 2019
260 South Carolina-B Win 15-9 1342.97 Mar 31st I 85 Rodeo 2019
279 Maryland-B Win 13-5 1358.09 Mar 31st I 85 Rodeo 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)