#172 East Carolina (5-7)

avg: 1063.43  •  sd: 60.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
- Charleston Win 13-9 833.41 Mar 18th College Southerns XXI
233 Florida-B Win 13-9 1198.45 Mar 18th College Southerns XXI
219 Georgia-B Win 12-8 1300.73 Mar 18th College Southerns XXI
52 Appalachian State Loss 9-15 1118.56 Mar 19th College Southerns XXI
189 Luther Win 15-9 1510.47 Mar 19th College Southerns XXI
268 Georgia Southern Win 14-4 1251.38 Mar 19th College Southerns XXI
106 Liberty Loss 4-15 742.93 Apr 1st Atlantic Coast Open 2023
71 Cornell Loss 5-12 903.63 Apr 1st Atlantic Coast Open 2023
45 Georgetown Loss 7-15 1096.69 Apr 1st Atlantic Coast Open 2023
162 American Loss 12-13 979.41 Apr 2nd Atlantic Coast Open 2023
168 Johns Hopkins Loss 12-13 961.67 Apr 2nd Atlantic Coast Open 2023
147 Connecticut Loss 13-15 948.32 Apr 2nd Atlantic Coast Open 2023
**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)