#168 Charleston (9-9)

avg: 1207.88  •  sd: 82.2  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
359 Georgia College** Win 13-5 992.73 Ignored Mar 15th Southerns 2025
233 Georgia Southern Win 13-4 1535.26 Mar 15th Southerns 2025
188 Georgia Tech-B Win 10-3 1717.81 Mar 15th Southerns 2025
263 Georgia-B Win 9-5 1351.43 Mar 15th Southerns 2025
233 Georgia Southern Win 15-5 1535.26 Mar 15th Southerns 2025
198 North Carolina-B Win 12-8 1517.34 Mar 29th Needle in a Ho Stack 2025
370 Morehouse** Win 13-2 914.4 Ignored Mar 29th Needle in a Ho Stack 2025
252 East Tennessee State Win 13-4 1470.51 Mar 29th Needle in a Ho Stack 2025
106 Appalachian State Loss 2-15 849.49 Mar 30th Needle in a Ho Stack 2025
233 Georgia Southern Win 15-7 1535.26 Mar 30th Needle in a Ho Stack 2025
98 Tennessee-Chattanooga Loss 6-13 872.22 Mar 30th Needle in a Ho Stack 2025
106 Appalachian State Loss 11-12 1324.49 Apr 12th Carolina D I Mens Conferences 2025
62 North Carolina State Loss 11-13 1422.49 Apr 12th Carolina D I Mens Conferences 2025
69 North Carolina-Charlotte Loss 5-8 1165.37 Apr 12th Carolina D I Mens Conferences 2025
35 South Carolina** Loss 3-13 1234.21 Ignored Apr 12th Carolina D I Mens Conferences 2025
106 Appalachian State Loss 11-15 1068.32 Apr 13th Carolina D I Mens Conferences 2025
172 East Carolina Loss 10-14 785.69 Apr 13th Carolina D I Mens Conferences 2025
239 Wake Forest Loss 4-15 319.66 Apr 13th Carolina D I Mens Conferences 2025
**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)