#244 Dickinson (10-7)

avg: 979.09  •  sd: 72.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
275 Central Connecticut State Win 8-4 1413.47 Feb 24th Bring The Huckus 2024
331 Rutgers-B Win 13-3 1196.28 Feb 24th Bring The Huckus 2024
318 Swarthmore Win 7-6 780.47 Feb 24th Bring The Huckus 2024
327 SUNY-Binghamton-B Win 8-3 1214.21 Feb 24th Bring The Huckus 2024
225 Colby Loss 10-15 582.08 Feb 25th Bring The Huckus 2024
401 Siena** Win 13-1 639.98 Ignored Mar 23rd King of New York 2024
327 SUNY-Binghamton-B Win 9-4 1214.21 Mar 24th King of New York 2024
374 New Jersey Tech Win 8-6 624.19 Mar 24th King of New York 2024
226 American Win 12-11 1158.12 Mar 30th Atlantic Coast Open 2024
209 Christopher Newport Loss 14-15 956.48 Mar 30th Atlantic Coast Open 2024
76 Massachusetts -B** Loss 5-15 1010.5 Ignored Mar 30th Atlantic Coast Open 2024
217 George Washington Loss 1-15 458.5 Mar 30th Atlantic Coast Open 2024
148 Johns Hopkins Loss 4-15 715.12 Mar 31st Atlantic Coast Open 2024
204 Virginia Commonwealth Loss 9-12 762.61 Mar 31st Atlantic Coast Open 2024
285 Penn State-Behrend Win 8-6 1100.94 Apr 13th West Penn D III Mens Conferences 2024
174 Grove City Loss 7-8 1099.99 Apr 13th West Penn D III Mens Conferences 2024
198 Messiah Win 10-7 1518.66 Apr 13th West Penn D III 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)