#28 South Carolina (13-6)

avg: 1541.36  •  sd: 107.25  •  top 16/20: 15.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
21 North Carolina State Loss 10-12 1403.4 Jan 21st Carolina Kickoff womens and nonbinary
55 Appalachian State Win 12-4 1831.85 Jan 21st Carolina Kickoff womens and nonbinary
142 North Carolina-B** Win 14-2 1168.24 Ignored Jan 21st Carolina Kickoff womens and nonbinary
25 Duke Win 7-6 1693.74 Jan 22nd Carolina Kickoff womens and nonbinary
1 North Carolina** Loss 1-15 2211.43 Ignored Jan 22nd Carolina Kickoff womens and nonbinary
61 Tennessee Win 15-1 1756.5 Feb 25th Commonwealth Cup Weekend2 2023
27 Notre Dame Win 15-6 2156.17 Feb 25th Commonwealth Cup Weekend2 2023
65 Carnegie Mellon Win 14-9 1596.54 Feb 25th Commonwealth Cup Weekend2 2023
13 Pittsburgh Loss 7-13 1253.08 Feb 26th Commonwealth Cup Weekend2 2023
32 Ohio State Win 9-7 1794.77 Feb 26th Commonwealth Cup Weekend2 2023
66 Case Western Reserve Win 13-1 1722.19 Feb 26th Commonwealth Cup Weekend2 2023
27 Notre Dame Loss 7-11 1089.28 Feb 26th Commonwealth Cup Weekend2 2023
67 Massachusetts Win 11-7 1586.54 Mar 25th Rodeo 2023
59 Ohio Win 10-5 1750.75 Mar 25th Rodeo 2023
25 Duke Loss 2-10 968.74 Mar 25th Rodeo 2023
182 Georgetown-B** Win 13-2 771.54 Ignored Mar 25th Rodeo 2023
213 Elon** Win 13-3 287.65 Ignored Mar 26th Rodeo 2023
21 North Carolina State Loss 7-10 1251.86 Mar 26th Rodeo 2023
57 Penn State Win 11-9 1474.13 Mar 26th Rodeo 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)