#93 Kennesaw State (13-9)

avg: 1188.04  •  sd: 55.13  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
211 Alabama-Huntsville** Win 13-3 1082.58 Ignored Jan 26th Clutch Classic 2019
98 Mississippi State Win 13-9 1546.72 Jan 26th Clutch Classic 2019
180 Georgia State Win 13-3 1230.73 Jan 26th Clutch Classic 2019
144 Tennessee Win 13-1 1496.06 Jan 27th Clutch Classic 2019
25 Clemson Loss 4-9 1272.28 Jan 27th Clutch Classic 2019
104 Boston College Win 11-7 1561.87 Jan 27th Clutch Classic 2019
144 Tennessee Win 10-2 1496.06 Feb 16th Luminous 2019
261 Emory-B** Win 13-0 657.5 Ignored Feb 16th Luminous 2019
180 Georgia State Win 9-1 1230.73 Feb 16th Luminous 2019
18 South Carolina Loss 3-7 1371.42 Feb 16th Luminous 2019
81 Ohio Loss 8-13 772.14 Feb 23rd Commonwealth Cup 2019
59 Duke Loss 0-13 845.96 Feb 23rd Commonwealth Cup 2019
223 Elon Win 13-7 940.75 Feb 23rd Commonwealth Cup 2019
161 Drexel Win 13-2 1410.99 Feb 24th Commonwealth Cup 2019
231 Pennsylvania-B** Win 9-1 917.18 Ignored Feb 24th Commonwealth Cup 2019
69 Notre Dame Loss 9-13 910.29 Mar 16th Tally Classic XIV
25 Clemson** Loss 5-13 1272.28 Ignored Mar 16th Tally Classic XIV
51 Florida State Loss 6-11 961.66 Mar 16th Tally Classic XIV
189 Tulane Win 13-6 1193.98 Mar 16th Tally Classic XIV
137 Illinois Win 11-7 1403.53 Mar 17th Tally Classic XIV
38 Florida Loss 7-11 1144.22 Mar 17th Tally Classic XIV
51 Florida State Loss 10-15 1054.75 Mar 17th Tally Classic XIV
**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)