#277 Salisbury (9-7)

avg: 779.58  •  sd: 83.04  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
325 Central Connecticut State Win 8-7 722.71 Mar 1st Garden State 2025
336 Pennsylvania Western Win 8-6 857.45 Mar 1st Garden State 2025
318 Massachusetts-Lowell Win 9-6 1030.82 Mar 1st Garden State 2025
310 Swarthmore Win 7-4 1133.51 Mar 1st Garden State 2025
95 Bowdoin** Loss 4-13 880.17 Ignored Mar 2nd Garden State 2025
197 Haverford Win 10-8 1341.14 Mar 2nd Garden State 2025
210 Penn State-B Win 3-2 1152.54 Mar 2nd Garden State 2025
244 College of New Jersey Win 9-7 1181.94 Mar 8th First State Invite
161 Delaware Loss 6-13 630.36 Mar 8th First State Invite
161 Delaware Loss 4-13 630.36 Mar 8th First State Invite
298 Maryland-Baltimore County Win 10-8 963.03 Mar 8th First State Invite
142 Davidson Loss 8-15 726.63 Apr 12th Atlantic Coast D III Mens Conferences 2025
33 Elon** Loss 2-15 1242.66 Ignored Apr 12th Atlantic Coast D III Mens Conferences 2025
282 Navy Loss 10-15 310.32 Apr 12th Atlantic Coast D III Mens Conferences 2025
365 High Point Win 15-9 876.37 Apr 13th Atlantic Coast D III Mens Conferences 2025
290 Mary Washington Loss 6-15 134.64 Apr 13th Atlantic Coast D III 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)