#27 South Carolina (16-10)

avg: 1848.18  •  sd: 46.5  •  top 16/20: 2.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
142 Carleton College-CHOP** Win 15-6 1783.5 Ignored Jan 28th Carolina Kickoff
84 Richmond Win 15-5 2049.92 Jan 28th Carolina Kickoff
20 North Carolina State Loss 11-15 1564.23 Jan 28th Carolina Kickoff
24 North Carolina-Charlotte Win 13-10 2222.62 Jan 28th Carolina Kickoff
33 Duke Win 15-12 2091.15 Jan 29th Carolina Kickoff
1 North Carolina Loss 6-15 1792.65 Jan 29th Carolina Kickoff
24 North Carolina-Charlotte Win 13-12 2019.48 Jan 29th Carolina Kickoff
52 Appalachian State Win 15-9 2149.53 Feb 11th Queen City Tune Up1
134 Carnegie Mellon Win 15-8 1801.12 Feb 11th Queen City Tune Up1
69 Maryland Win 14-10 1938.66 Feb 11th Queen City Tune Up1
13 Tufts Loss 11-15 1687.06 Feb 11th Queen City Tune Up1
25 North Carolina-Wilmington Loss 8-11 1518.65 Feb 12th Queen City Tune Up1
37 McGill Win 13-11 2002.21 Feb 25th Easterns Qualifier 2023
77 Temple Win 13-7 2037.85 Feb 25th Easterns Qualifier 2023
56 James Madison Win 13-9 2018.21 Feb 25th Easterns Qualifier 2023
131 Georgia State** Win 13-4 1842.58 Ignored Feb 25th Easterns Qualifier 2023
71 Cornell Win 13-9 1922.16 Feb 26th Easterns Qualifier 2023
24 North Carolina-Charlotte Win 15-11 2275.64 Feb 26th Easterns Qualifier 2023
41 William & Mary Win 15-11 2100.04 Feb 26th Easterns Qualifier 2023
11 Brown Loss 9-13 1656.15 Apr 1st Easterns 2023
14 Carleton College Loss 8-12 1608.72 Apr 1st Easterns 2023
34 Michigan Win 13-10 2116.97 Apr 1st Easterns 2023
9 Oregon Loss 10-13 1809 Apr 1st Easterns 2023
13 Tufts Loss 7-15 1468.22 Apr 2nd Easterns 2023
21 Northeastern Loss 11-15 1526.01 Apr 2nd Easterns 2023
23 Wisconsin Loss 11-15 1513.35 Apr 2nd Easterns 2023
**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)