#66 Kennesaw State (17-5)

avg: 1458.01  •  sd: 83.08  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
335 Southern Mississippi** Win 13-4 1028.37 Ignored Jan 20th T Town Throwdown XIV Open
153 Xavier Win 12-7 1636 Jan 20th T Town Throwdown XIV Open
97 Alabama Win 12-7 1868.44 Jan 20th T Town Throwdown XIV Open
88 Alabama-Huntsville Loss 6-13 788.31 Jan 20th T Town Throwdown XIV Open
120 Mississippi State Win 15-12 1561.79 Jan 21st T Town Throwdown XIV Open
94 Kentucky Win 15-8 1927.47 Jan 21st T Town Throwdown XIV Open
88 Alabama-Huntsville Win 15-8 1953.12 Jan 21st T Town Throwdown XIV Open
381 Georgia Gwinnett** Win 13-4 815.23 Ignored Jan 27th Clutch Classic 2018
140 Florida Tech Win 10-8 1430.14 Jan 27th Clutch Classic 2018
272 Miami Win 13-7 1259.22 Jan 27th Clutch Classic 2018
282 Wingate Win 13-6 1262.96 Jan 27th Clutch Classic 2018
244 Berry** Win 15-1 1383.64 Ignored Jan 28th Clutch Classic 2018
376 Tulane-B** Win 15-0 878 Ignored Jan 28th Clutch Classic 2018
140 Florida Tech Win 15-7 1767.47 Jan 28th Clutch Classic 2018
61 James Madison Win 13-12 1597.52 Feb 17th Easterns Qualifier 2018
48 Dartmouth Win 10-7 1955.09 Feb 17th Easterns Qualifier 2018
149 Davidson Loss 11-12 1015.86 Feb 17th Easterns Qualifier 2018
151 George Mason Win 13-6 1716.84 Feb 17th Easterns Qualifier 2018
98 Clemson Win 10-8 1600.71 Feb 17th Easterns Qualifier 2018
48 Dartmouth Loss 6-15 965.43 Feb 18th Easterns Qualifier 2018
34 William & Mary Loss 9-14 1174.33 Feb 18th Easterns Qualifier 2018
46 South Carolina Loss 7-15 979.36 Feb 18th Easterns Qualifier 2018
**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)