#223 Colby (8-10)

avg: 982.72  •  sd: 80.3  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
214 MIT Loss 6-8 720.18 Feb 22nd Bring The Huckus 2025
113 West Chester Loss 5-9 863.58 Feb 22nd Bring The Huckus 2025
179 Dickinson Loss 5-13 566.25 Feb 22nd Bring The Huckus 2025
265 Drexel Win 9-6 1235.3 Feb 22nd Bring The Huckus 2025
160 Ithaca Loss 6-13 630.7 Feb 23rd Bring The Huckus 2025
161 Delaware Loss 7-8 1105.36 Feb 23rd Bring The Huckus 2025
235 Skidmore Win 8-3 1527 Feb 23rd Bring The Huckus 2025
312 Amherst Win 8-2 1228.94 Mar 8th Grand Northeast Kickoff 2025
133 Bates Win 6-5 1450.44 Mar 8th Grand Northeast Kickoff 2025
262 Brown-B Loss 4-8 264.19 Mar 8th Grand Northeast Kickoff 2025
262 Brown-B Win 15-5 1429 Mar 9th Grand Northeast Kickoff 2025
399 Middlebury-B** Win 12-3 686 Ignored Mar 9th Grand Northeast Kickoff 2025
215 Northeastern-B Loss 7-8 890.78 Mar 9th Grand Northeast Kickoff 2025
385 New Hampshire** Win 11-4 802.59 Ignored Mar 9th Grand Northeast Kickoff 2025
133 Bates Loss 8-14 789.41 Apr 12th North New England D III Mens Conferences 2025
36 Middlebury** Loss 5-15 1218.97 Ignored Apr 12th North New England D III Mens Conferences 2025
70 Dartmouth Loss 7-11 1151.61 Apr 12th North New England D III Mens Conferences 2025
399 Middlebury-B** Win 12-4 686 Ignored Apr 13th North New England 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)