#175 Delaware (6-13)

avg: 1221.88  •  sd: 67.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
102 Connecticut Loss 8-9 1370.12 Mar 2nd No Sleep till Brooklyn 2024
93 Princeton Loss 8-9 1413.03 Mar 2nd No Sleep till Brooklyn 2024
120 Syracuse Loss 6-11 856.57 Mar 2nd No Sleep till Brooklyn 2024
102 Connecticut Loss 6-8 1194.63 Mar 3rd No Sleep till Brooklyn 2024
201 MIT Win 11-9 1365.91 Mar 3rd No Sleep till Brooklyn 2024
93 Princeton Loss 10-11 1413.03 Mar 3rd No Sleep till Brooklyn 2024
150 West Chester Loss 5-11 714.53 Mar 23rd Garden State 2024
198 Messiah Win 10-7 1518.66 Mar 23rd Garden State 2024
212 West Virginia Loss 6-7 954.5 Mar 24th Garden State 2024
170 Villanova Loss 7-9 972.63 Mar 24th Garden State 2024
198 Messiah Loss 6-11 582.3 Mar 24th Garden State 2024
272 Rowan Win 11-7 1323.68 Mar 24th Garden State 2024
170 Villanova Loss 7-8 1126.96 Mar 24th Garden State 2024
87 Georgetown Loss 9-15 1038.92 Apr 20th Colonial D I Mens Conferences 2024
126 Towson Loss 9-12 1043.72 Apr 20th Colonial D I Mens Conferences 2024
217 George Washington Win 15-8 1623.31 Apr 20th Colonial D I Mens Conferences 2024
226 American Win 15-8 1597.93 Apr 21st Colonial D I Mens Conferences 2024
148 Johns Hopkins Win 13-11 1543.96 Apr 21st Colonial D I Mens Conferences 2024
126 Towson Loss 11-12 1264.09 Apr 21st Colonial D I Mens Conferences 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)