#162 Delaware (14-11)

avg: 1213.28  •  sd: 52.17  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
196 Haverford Loss 8-9 943.39 Feb 22nd Bring The Huckus 2025
331 Hofstra** Win 10-3 1170.89 Ignored Feb 22nd Bring The Huckus 2025
161 Ithaca Loss 9-11 965.84 Feb 22nd Bring The Huckus 2025
314 Rowan Win 10-6 1119.78 Feb 22nd Bring The Huckus 2025
231 Colby Win 8-7 1052.19 Feb 23rd Bring The Huckus 2025
178 Dickinson Loss 8-13 656.64 Feb 23rd Bring The Huckus 2025
222 MIT Win 11-5 1576.13 Feb 23rd Bring The Huckus 2025
243 College of New Jersey Win 13-2 1493.37 Mar 8th First State Invite
277 Salisbury Win 13-6 1369.52 Mar 8th First State Invite
300 Maryland-Baltimore County Win 13-6 1285.62 Mar 8th First State Invite
277 Salisbury Win 13-4 1369.52 Mar 8th First State Invite
106 Columbia Win 11-10 1553.85 Mar 29th East Coast Invite 2025
55 Maryland Loss 6-14 1077.37 Mar 29th East Coast Invite 2025
74 Temple Loss 5-13 984.07 Mar 29th East Coast Invite 2025
229 Harvard Win 11-7 1405.93 Mar 29th East Coast Invite 2025
147 SUNY-Binghamton Loss 7-9 986.83 Mar 30th East Coast Invite 2025
166 Pennsylvania Loss 9-11 944.87 Mar 30th East Coast Invite 2025
142 Pittsburgh-B Loss 5-9 755.93 Mar 30th East Coast Invite 2025
181 American Win 10-6 1635.12 Apr 12th Colonial D I Mens Conferences 2025
232 George Washington Win 8-6 1225.2 Apr 12th Colonial D I Mens Conferences 2025
64 Georgetown Loss 6-14 1028.4 Apr 12th Colonial D I Mens Conferences 2025
55 Maryland Loss 7-14 1094.49 Apr 12th Colonial D I Mens Conferences 2025
232 George Washington Win 15-8 1489.52 Apr 13th Colonial D I Mens Conferences 2025
155 Johns Hopkins Win 10-8 1501.18 Apr 13th Colonial D I Mens Conferences 2025
55 Maryland Loss 9-12 1332.01 Apr 13th Colonial 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)