#1 North Carolina (24-2)

avg: 2288.56  •  sd: 47.23  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
38 Duke** Win 15-6 2190.76 Ignored Jan 26th Carolina Kickoff 2024
84 Appalachian State** Win 15-3 1926.8 Ignored Jan 27th Carolina Kickoff 2024
16 Penn State Win 15-12 2221.72 Jan 28th Carolina Kickoff 2024
29 South Carolina Win 15-8 2248.72 Jan 28th Carolina Kickoff 2024
13 North Carolina State Win 15-10 2400.2 Jan 28th Carolina Kickoff 2024
91 Indiana** Win 15-4 1870.81 Ignored Feb 10th Queen City Tune Up 2024
25 McGill Win 13-9 2192.82 Feb 10th Queen City Tune Up 2024
28 North Carolina-Wilmington Win 12-10 1972.63 Feb 10th Queen City Tune Up 2024
66 Virginia Win 15-7 1994.17 Feb 10th Queen City Tune Up 2024
12 Alabama-Huntsville Win 15-12 2294.17 Feb 11th Queen City Tune Up 2024
34 Ohio State** Win 13-5 2241.87 Ignored Feb 11th Queen City Tune Up 2024
16 Penn State Win 15-9 2436.71 Feb 11th Queen City Tune Up 2024
10 Carleton College Win 13-10 2338.48 Mar 2nd Smoky Mountain Invite 2024
26 Utah State Win 13-9 2187.97 Mar 2nd Smoky Mountain Invite 2024
8 Vermont Win 13-7 2596.13 Mar 2nd Smoky Mountain Invite 2024
92 Tennessee** Win 15-5 1867.11 Ignored Mar 2nd Smoky Mountain Invite 2024
3 Colorado Win 15-11 2624.29 Mar 3rd Smoky Mountain Invite 2024
4 Massachusetts Loss 13-15 2020.78 Mar 3rd Smoky Mountain Invite 2024
7 Pittsburgh Win 13-9 2511.71 Mar 3rd Smoky Mountain Invite 2024
42 Michigan Win 13-7 2123.95 Mar 30th Easterns 2024
11 Minnesota Win 13-9 2420.81 Mar 30th Easterns 2024
34 Ohio State Win 13-7 2199.4 Mar 30th Easterns 2024
8 Vermont Win 13-7 2596.13 Mar 30th Easterns 2024
9 Brown Win 15-12 2325.57 Mar 31st Easterns 2024
7 Pittsburgh Loss 12-15 1792.65 Mar 31st Easterns 2024
8 Vermont Win 15-10 2492.2 Mar 31st Easterns 2024
**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)