#88 Georgetown (7-12)

avg: 1308.67  •  sd: 66.94  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
45 Elon Loss 10-13 1276.9 Feb 1st Carolina Kickoff mens 2025
21 Georgia Tech Loss 7-12 1334.28 Feb 1st Carolina Kickoff mens 2025
32 Virginia Loss 12-13 1554.87 Feb 1st Carolina Kickoff mens 2025
96 Appalachian State Win 13-11 1503.26 Feb 2nd Carolina Kickoff mens 2025
97 Duke Loss 11-13 1044.58 Feb 2nd Carolina Kickoff mens 2025
45 Elon Loss 11-14 1291.71 Feb 2nd Carolina Kickoff mens 2025
44 Emory Loss 6-13 1008.19 Feb 22nd Easterns Qualifier 2025
87 Temple Loss 10-13 982.78 Feb 22nd Easterns Qualifier 2025
37 North Carolina-Wilmington Loss 9-13 1216.5 Feb 22nd Easterns Qualifier 2025
102 Syracuse Win 11-4 1860.39 Feb 22nd Easterns Qualifier 2025
66 Dartmouth Loss 7-15 852.25 Feb 23rd Easterns Qualifier 2025
84 Ohio State Win 15-12 1620.16 Feb 23rd Easterns Qualifier 2025
49 North Carolina State Loss 10-14 1166.1 Feb 23rd Easterns Qualifier 2025
52 William & Mary Loss 9-10 1426.49 Mar 29th East Coast Invite 2025
236 NYU** Win 15-3 1249.53 Ignored Mar 29th East Coast Invite 2025
179 Pennsylvania Win 13-5 1511.9 Mar 29th East Coast Invite 2025
192 Princeton Win 13-6 1454.4 Mar 29th East Coast Invite 2025
98 Boston College Loss 8-15 706.89 Mar 30th East Coast Invite 2025
131 Pittsburgh-B Win 15-7 1721.3 Mar 30th East Coast Invite 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)