#194 Kennesaw State (5-6)

avg: 858.55  •  sd: 80.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
190 Berry Win 13-10 1206.52 Jan 25th Clutch Classic 2020
342 Kentucky-B Win 13-6 596.97 Jan 25th Clutch Classic 2020
176 Xavier Win 11-10 1052.06 Jan 25th Clutch Classic 2020
300 Belmont University Win 13-6 939.97 Jan 25th Clutch Classic 2020
197 Georgia Southern Win 10-6 1332.42 Jan 26th Clutch Classic 2020
191 Georgia College Loss 6-9 451.26 Jan 26th Clutch Classic 2020
91 Indiana Loss 5-13 670.12 Feb 29th Easterns Qualifier 2020
23 William & Mary** Loss 1-13 1188.22 Ignored Feb 29th Easterns Qualifier 2020
48 Temple** Loss 1-13 902.96 Ignored Feb 29th Easterns Qualifier 2020
67 Wisconsin-Milwaukee Loss 6-13 791.64 Feb 29th Easterns Qualifier 2020
131 Johns Hopkins Loss 11-14 781.23 Mar 1st Easterns Qualifier 2020
**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)