#158 Kennesaw State (7-12)

avg: 1010.72  •  sd: 47.34  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
119 Berry Loss 13-15 958.14 Feb 10th Golden Triangle Invitational
192 Harding Win 13-11 1079.95 Feb 10th Golden Triangle Invitational
139 LSU Loss 8-11 719 Feb 10th Golden Triangle Invitational
137 Union (Tennessee) Loss 7-11 623.61 Feb 10th Golden Triangle Invitational
76 Purdue Loss 1-13 757.22 Feb 11th Golden Triangle Invitational
60 Temple Loss 8-10 1172.36 Feb 24th Easterns Qualifier 2024
111 SUNY-Binghamton Loss 6-12 612.41 Feb 24th Easterns Qualifier 2024
29 South Carolina** Loss 3-13 1083.91 Ignored Feb 24th Easterns Qualifier 2024
34 Ohio State** Loss 3-13 1041.87 Ignored Feb 24th Easterns Qualifier 2024
154 Harvard Win 12-11 1148.19 Feb 25th Easterns Qualifier 2024
61 William & Mary Loss 10-13 1103.86 Feb 25th Easterns Qualifier 2024
76 Purdue Loss 9-12 1011.85 Feb 25th Easterns Qualifier 2024
111 SUNY-Binghamton Win 7-6 1316.72 Feb 25th Easterns Qualifier 2024
296 South Carolina-B** Win 12-2 969.12 Ignored Mar 23rd Needle in a Ho Stack 2024
116 Liberty Loss 9-10 1057.96 Mar 23rd Needle in a Ho Stack 2024
77 Cedarville Loss 7-12 834.99 Mar 24th Needle in a Ho Stack 2024
210 Charleston Win 10-7 1166.53 Mar 24th Needle in a Ho Stack 2024
189 East Carolina Win 10-6 1358.2 Mar 24th Needle in a Ho Stack 2024
271 High Point Win 13-2 1111.1 Mar 24th Needle in a Ho Stack 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)