#231 Colby (11-12)

avg: 927.19  •  sd: 64.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
222 MIT Loss 6-8 675.64 Feb 22nd Bring The Huckus 2025
113 West Chester Loss 5-9 846.9 Feb 22nd Bring The Huckus 2025
178 Dickinson Loss 5-13 552.8 Feb 22nd Bring The Huckus 2025
266 Drexel Win 9-6 1223.87 Feb 22nd Bring The Huckus 2025
161 Ithaca Loss 6-13 615.04 Feb 23rd Bring The Huckus 2025
162 Delaware Loss 7-8 1088.28 Feb 23rd Bring The Huckus 2025
235 Skidmore Win 8-3 1515.2 Feb 23rd Bring The Huckus 2025
293 Amherst Win 8-2 1321.01 Mar 8th Grand Northeast Kickoff 2025
123 Bates Win 6-5 1469.49 Mar 8th Grand Northeast Kickoff 2025
253 Brown-B Loss 4-8 292.3 Mar 8th Grand Northeast Kickoff 2025
253 Brown-B Win 15-5 1457.11 Mar 9th Grand Northeast Kickoff 2025
392 Middlebury-B** Win 12-3 738.34 Ignored Mar 9th Grand Northeast Kickoff 2025
185 Northeastern-B Loss 7-8 991.27 Mar 9th Grand Northeast Kickoff 2025
376 New Hampshire** Win 11-4 874.79 Ignored Mar 9th Grand Northeast Kickoff 2025
123 Bates Loss 8-14 808.46 Apr 12th North New England D III Mens Conferences 2025
40 Middlebury** Loss 5-15 1174.28 Ignored Apr 12th North New England D III Mens Conferences 2025
70 Dartmouth Loss 7-11 1129.06 Apr 12th North New England D III Mens Conferences 2025
392 Middlebury-B** Win 12-4 738.34 Ignored Apr 13th North New England D III Mens Conferences 2025
310 Bentley Win 13-12 761.01 May 3rd New England D III College Mens Regionals 2025
90 Bowdoin Loss 6-13 878.93 May 3rd New England D III College Mens Regionals 2025
344 Holy Cross Win 11-10 623.32 May 3rd New England D III College Mens Regionals 2025
123 Bates Loss 9-14 870.63 May 4th New England D III College Mens Regionals 2025
250 Worcester Polytechnic Win 12-11 992.13 May 4th New England D III College Mens Regionals 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)