#40 Kennesaw State (11-7)

avg: 2017.59  •  sd: 137.77  •  top 16/20: 4.8%

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# Opponent Result Game Rating Status Date Event
231 Tulane** Win 13-1 1239.41 Ignored Jan 27th Clutch Classic 2018
130 Mississippi State** Win 13-4 1892.44 Ignored Jan 27th Clutch Classic 2018
237 Georgia Tech-B** Win 13-4 1174.53 Ignored Jan 27th Clutch Classic 2018
267 Emory-B** Win 13-0 506.06 Ignored Jan 27th Clutch Classic 2018
243 Georgia State** Win 15-1 1105.1 Ignored Jan 28th Clutch Classic 2018
84 Emory Win 13-4 2216.21 Jan 28th Clutch Classic 2018
107 LSU Win 10-5 2040.09 Jan 28th Clutch Classic 2018
42 Wisconsin Loss 5-9 1474.64 Feb 3rd Queen City Tune Up 2018 College Women
7 Tufts Loss 3-11 1908.79 Feb 3rd Queen City Tune Up 2018 College Women
10 Pittsburgh Loss 2-13 1881.75 Feb 3rd Queen City Tune Up 2018 College Women
15 North Carolina State Loss 9-10 2228.08 Feb 3rd Queen City Tune Up 2018 College Women
25 Notre Dame Loss 9-11 1890.5 Mar 10th Tally Classic XIII
47 Harvard Loss 9-10 1852.97 Mar 10th Tally Classic XIII
243 Georgia State** Win 11-4 1105.1 Ignored Mar 10th Tally Classic XIII
54 Florida State Win 13-11 2084.9 Mar 10th Tally Classic XIII
41 Georgia Tech Loss 11-14 1696.08 Mar 10th Tally Classic XIII
47 Harvard Win 15-9 2493.45 Mar 11th Tally Classic XIII
32 Florida Win 14-12 2301.2 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)