#218 Middlebury-B (12-11)

avg: 1054.71  •  sd: 61.94  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
76 Massachusetts -B Loss 1-11 1010.5 Mar 2nd Grand Northeast Kickoff
312 Western New England Win 15-7 1273.67 Mar 2nd Grand Northeast Kickoff
138 Tufts-B Loss 7-15 763.74 Mar 2nd Grand Northeast Kickoff
279 Amherst Loss 9-15 320.03 Mar 3rd Grand Northeast Kickoff
225 Colby Loss 10-14 636.98 Mar 3rd Grand Northeast Kickoff
312 Western New England Win 13-11 902.51 Mar 3rd Grand Northeast Kickoff
283 Hofstra Win 11-6 1355.97 Mar 30th Northeast Classic 2024
397 SUNY-Albany-B** Win 13-3 692.79 Ignored Mar 30th Northeast Classic 2024
240 SUNY-Albany Win 10-7 1377.45 Mar 30th Northeast Classic 2024
159 Rhode Island Loss 6-9 869.58 Mar 31st Northeast Classic 2024
318 Swarthmore Win 11-5 1255.47 Mar 31st Northeast Classic 2024
240 SUNY-Albany Win 10-8 1250.45 Mar 31st Northeast Classic 2024
267 SUNY-Geneseo Win 7-6 996.49 Mar 31st Northeast Classic 2024
75 Dartmouth Loss 9-13 1193.92 Apr 13th North New England D III Mens Conferences 2024
367 Dartmouth-B Win 15-9 933.02 Apr 13th North New England D III Mens Conferences 2024
115 Bowdoin Loss 5-11 836.94 Apr 14th North New England D III Mens Conferences 2024
225 Colby Win 12-7 1556.2 Apr 14th North New England D III Mens Conferences 2024
25 Middlebury** Loss 6-15 1404.07 Ignored May 4th New England D III College Mens Regionals 2024
55 Williams Loss 9-13 1331 May 4th New England D III College Mens Regionals 2024
308 Stonehill Win 13-10 1010.07 May 4th New England D III College Mens Regionals 2024
238 Roger Williams Win 11-9 1238.66 May 4th New England D III College Mens Regionals 2024
225 Colby Loss 8-11 670.08 May 5th New England D III College Mens Regionals 2024
189 Worcester Polytechnic Institute Loss 9-10 1034.5 May 5th New England D III College Mens Regionals 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)