#1 North Carolina (21-0)

avg: 2345.34  •  sd: 40.07  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
14 Florida Win 13-8 2382.98 Jan 20th Carolina Kickoff 2018 NC Ultimate
69 Carleton College-GoP Win 13-7 2006.99 Jan 20th Carolina Kickoff 2018 NC Ultimate
12 North Carolina State Win 13-11 2147.7 Jan 21st Carolina Kickoff 2018 NC Ultimate
37 Central Florida Win 13-7 2192.28 Jan 21st Carolina Kickoff 2018 NC Ultimate
91 Penn State Win 13-6 1973.9 Jan 21st Carolina Kickoff 2018 NC Ultimate
12 North Carolina State Win 10-5 2492.76 Feb 3rd Queen City Tune Up 2018 College Open
9 Georgia Win 11-7 2416.17 Feb 3rd Queen City Tune Up 2018 College Open
22 Tufts Win 11-5 2350.18 Feb 3rd Queen City Tune Up 2018 College Open
28 Carnegie Mellon Win 11-5 2318.65 Feb 3rd Queen City Tune Up 2018 College Open
62 Vermont Win 11-6 2012.53 Feb 3rd Queen City Tune Up 2018 College Open
7 Pittsburgh Win 13-8 2483.62 Mar 3rd Stanford Invite 2018
32 California Win 13-7 2253.33 Mar 3rd Stanford Invite 2018
18 Brigham Young Win 13-10 2181.53 Mar 3rd Stanford Invite 2018
6 Brown Win 13-7 2604.24 Mar 4th Stanford Invite 2018
3 Oregon Win 15-14 2313.77 Mar 4th Stanford Invite 2018
2 Carleton College Win 13-8 2724.36 Mar 4th Stanford Invite 2018
12 North Carolina State Win 13-9 2337.43 Mar 20th Atlantic Coast Showcase ACS NCSU vs UNC
33 Maryland** Win 15-6 2284.28 Ignored Mar 31st Easterns 2018
14 Florida Win 15-8 2451.62 Mar 31st Easterns 2018
36 Michigan** Win 15-5 2238.31 Ignored Mar 31st Easterns 2018
13 Wisconsin Win 15-7 2517.12 Mar 31st Easterns 2018
**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)