#81 North Carolina-Charlotte (7-11)

avg: 1322.26  •  sd: 80.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
54 Carleton College-CHOP Win 13-11 1766.9 Feb 1st Carolina Kickoff mens 2025
49 North Carolina State Loss 11-13 1335.96 Feb 1st Carolina Kickoff mens 2025
87 Temple Win 13-8 1807.08 Feb 1st Carolina Kickoff mens 2025
27 South Carolina Loss 12-15 1479.11 Feb 2nd Carolina Kickoff mens 2025
32 Virginia Loss 10-12 1441.75 Feb 2nd Carolina Kickoff mens 2025
37 North Carolina-Wilmington Loss 8-14 1099.03 Feb 2nd Carolina Kickoff mens 2025
69 Auburn Loss 9-12 1083.03 Feb 15th Queen City Tune Up 2025
3 North Carolina** Loss 2-13 1606.12 Ignored Feb 15th Queen City Tune Up 2025
101 Yale Loss 7-13 704.95 Feb 15th Queen City Tune Up 2025
60 Michigan State Loss 6-8 1185.86 Feb 16th Queen City Tune Up 2025
52 William & Mary Loss 7-10 1161.82 Feb 16th Queen City Tune Up 2025
180 American Win 11-6 1452.43 Mar 22nd Atlantic Coast Open 2025
171 Dickinson Win 11-7 1420.1 Mar 22nd Atlantic Coast Open 2025
184 East Carolina Win 12-6 1469.02 Mar 22nd Atlantic Coast Open 2025
357 George Washington-B** Win 15-1 628.03 Ignored Mar 22nd Atlantic Coast Open 2025
105 Liberty Loss 6-11 686.22 Mar 23rd Atlantic Coast Open 2025
115 Vermont-B Win 15-11 1575.88 Mar 23rd Atlantic Coast Open 2025
43 Virginia Tech Loss 12-13 1487.2 Mar 23rd Atlantic Coast Open 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)