#198 North Carolina-B (14-10)

avg: 1076.19  •  sd: 66.25  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
116 Denver Loss 6-9 965.57 Feb 15th 2025 Commonwealth Cup Weekend 1
33 Elon** Loss 0-11 1242.66 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
256 Illinois-B Win 9-6 1279.39 Feb 15th 2025 Commonwealth Cup Weekend 1
178 Ohio Loss 3-11 566.75 Feb 15th 2025 Commonwealth Cup Weekend 1
227 Michigan-B Loss 8-9 836.98 Feb 16th 2025 Commonwealth Cup Weekend 1
239 Wake Forest Loss 6-9 501.09 Feb 16th 2025 Commonwealth Cup Weekend 1
122 Clemson Win 12-11 1486.6 Mar 1st Joint Summit 2025
344 Clemson-B Win 15-4 1079.07 Mar 1st Joint Summit 2025
184 Georgia State Win 12-9 1479.87 Mar 1st Joint Summit 2025
330 South Carolina-B Win 11-4 1175.32 Mar 1st Joint Summit 2025
126 Central Florida Loss 12-15 1045.99 Mar 2nd Joint Summit 2025
184 Georgia State Win 15-10 1588.11 Mar 2nd Joint Summit 2025
209 Cedarville Win 9-7 1310.74 Mar 29th Needle in a Ho Stack 2025
168 Charleston Loss 8-12 766.73 Mar 29th Needle in a Ho Stack 2025
252 East Tennessee State Win 13-8 1366.67 Mar 29th Needle in a Ho Stack 2025
370 Morehouse** Win 13-0 914.4 Ignored Mar 29th Needle in a Ho Stack 2025
106 Appalachian State Loss 10-15 995.88 Mar 30th Needle in a Ho Stack 2025
172 East Carolina Win 11-5 1784.39 Mar 30th Needle in a Ho Stack 2025
252 East Tennessee State Loss 11-13 641.67 Mar 30th Needle in a Ho Stack 2025
379 James Madison-B** Win 14-4 846.71 Ignored Apr 12th Southern Atlantic Coast Dev Mens Conferences 2025
264 Virginia Tech-B Win 13-10 1149.38 Apr 12th Southern Atlantic Coast Dev Mens Conferences 2025
330 South Carolina-B Win 12-11 700.32 Apr 12th Southern Atlantic Coast Dev Mens Conferences 2025
203 North Carolina State-B Loss 8-9 930.73 Apr 13th Southern Atlantic Coast Dev Mens Conferences 2025
264 Virginia Tech-B Win 13-12 946.24 Apr 13th Southern Atlantic Coast Dev Mens Conferences 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)