#98 Clemson (7-11)

avg: 1338.04  •  sd: 65.54  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
347 Radford** Win 15-5 978.28 Ignored Jan 27th Joint Summit XXXIII College Open
223 High Point Win 15-5 1463.1 Jan 27th Joint Summit XXXIII College Open
11 Emory Loss 6-12 1341.36 Jan 27th Joint Summit XXXIII College Open
61 James Madison Loss 8-13 976.36 Feb 17th Easterns Qualifier 2018
48 Dartmouth Loss 9-11 1316.22 Feb 17th Easterns Qualifier 2018
149 Davidson Win 12-7 1661.37 Feb 17th Easterns Qualifier 2018
66 Kennesaw State Loss 8-10 1195.35 Feb 17th Easterns Qualifier 2018
151 George Mason Win 13-3 1716.84 Feb 17th Easterns Qualifier 2018
64 North Carolina-Charlotte Loss 11-13 1233.66 Feb 18th Easterns Qualifier 2018
51 Ohio State Loss 11-13 1308.85 Feb 18th Easterns Qualifier 2018
102 Richmond Loss 8-12 885.73 Feb 18th Easterns Qualifier 2018
52 Harvard Loss 12-13 1411.01 Mar 10th Tally Classic XIII
120 Mississippi State Win 13-7 1818.82 Mar 10th Tally Classic XIII
50 Notre Dame Loss 12-14 1318.33 Mar 10th Tally Classic XIII
272 Miami Win 13-7 1259.22 Mar 10th Tally Classic XIII
9 Georgia** Loss 3-13 1349.28 Ignored Mar 10th Tally Classic XIII
120 Mississippi State Loss 10-15 807.69 Mar 11th Tally Classic XIII
168 South Florida Win 15-6 1663.99 Mar 11th Tally Classic XIII
**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)