#25 South Carolina (19-9)

avg: 1786.69  •  sd: 55.4  •  top 16/20: 5.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
102 Georgetown Win 12-9 1696.55 Jan 25th Carolina Kickoff 2019
52 Notre Dame Loss 10-11 1501.67 Jan 26th Carolina Kickoff 2019
119 Clemson Win 12-6 1862.86 Jan 26th Carolina Kickoff 2019
69 Emory Loss 8-10 1245.79 Jan 26th Carolina Kickoff 2019
85 Richmond Win 15-4 2029.7 Jan 27th Carolina Kickoff 2019
102 Georgetown Win 14-13 1476.18 Jan 27th Carolina Kickoff 2019
31 Texas A&M Win 12-10 1986.53 Feb 8th Florida Warm Up 2019
106 Illinois State Win 12-10 1565.46 Feb 8th Florida Warm Up 2019
72 Alabama-Huntsville Win 13-10 1812.13 Feb 8th Florida Warm Up 2019
83 Rutgers Win 10-8 1695.64 Feb 9th Florida Warm Up 2019
28 Northeastern Loss 9-12 1430.47 Feb 9th Florida Warm Up 2019
55 Florida State Win 13-4 2211.67 Feb 9th Florida Warm Up 2019
27 LSU Loss 10-15 1324.13 Feb 9th Florida Warm Up 2019
48 Kennesaw State Loss 12-13 1521.49 Feb 10th Florida Warm Up 2019
28 Northeastern Loss 6-11 1229.14 Feb 10th Florida Warm Up 2019
11 North Carolina State Loss 8-13 1531.41 Mar 9th Classic City Invite 2019
20 Tufts Win 13-12 1989.15 Mar 9th Classic City Invite 2019
61 Tennessee Win 12-10 1792.31 Mar 9th Classic City Invite 2019
22 Georgia Win 13-9 2253.06 Mar 10th Classic City Invite 2019
4 Pittsburgh Loss 8-11 1819.31 Mar 10th Classic City Invite 2019
9 Massachusetts Loss 8-9 1940.5 Mar 10th Classic City Invite 2019
256 Georgia-B** Win 13-2 1431.15 Ignored Mar 23rd College Southerns XVIII
136 South Florida Win 13-6 1837.03 Mar 23rd College Southerns XVIII
257 Charleston** Win 13-4 1430.33 Ignored Mar 23rd College Southerns XVIII
146 North Carolina-Asheville Win 13-5 1788.17 Mar 23rd College Southerns XVIII
69 Emory Win 15-8 2073.27 Mar 24th College Southerns XVIII
78 Carleton College-GoP Win 15-7 2057.72 Mar 24th College Southerns XVIII
35 Middlebury Win 15-11 2107.66 Mar 24th College Southerns XVIII
**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)