#187 North Carolina-B (10-9)

avg: 864.39  •  sd: 78.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
124 Denver Loss 6-9 727.79 Feb 15th 2025 Commonwealth Cup Weekend 1
45 Elon** Loss 0-11 1005.05 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
256 Illinois-B Win 9-6 999.1 Feb 15th 2025 Commonwealth Cup Weekend 1
164 Ohio Loss 3-11 376.87 Feb 15th 2025 Commonwealth Cup Weekend 1
241 Michigan-B Loss 8-9 517.3 Feb 16th 2025 Commonwealth Cup Weekend 1
255 Wake Forest Loss 6-9 167.92 Feb 16th 2025 Commonwealth Cup Weekend 1
127 Clemson Win 12-11 1259.14 Mar 1st Joint Summit 2025
350 Clemson-B** Win 15-4 710.41 Ignored Mar 1st Joint Summit 2025
198 Georgia State Win 12-9 1180.67 Mar 1st Joint Summit 2025
344 South Carolina-B** Win 11-4 729.6 Ignored Mar 1st Joint Summit 2025
119 Central Florida Loss 12-15 880.89 Mar 2nd Joint Summit 2025
198 Georgia State Win 15-10 1288.91 Mar 2nd Joint Summit 2025
249 Cedarville Win 9-7 892.14 Mar 29th Needle in a Ho Stack 2025
130 Charleston Loss 8-12 682.51 Mar 29th Needle in a Ho Stack 2025
257 East Tennessee State Win 13-8 1076.4 Mar 29th Needle in a Ho Stack 2025
370 Morehouse** Win 13-0 525.06 Ignored Mar 29th Needle in a Ho Stack 2025
96 Appalachian State Loss 10-15 820.82 Mar 30th Needle in a Ho Stack 2025
184 East Carolina Win 11-5 1489.71 Mar 30th Needle in a Ho Stack 2025
257 East Tennessee State Loss 11-13 351.4 Mar 30th Needle in a Ho Stack 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)