#246 Connecticut College (11-6)

avg: 885.45  •  sd: 82.31  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
355 Army Win 12-4 1013.85 Mar 1st Garden State 2025
359 Cornell-B Win 14-4 997.78 Mar 1st Garden State 2025
389 Stevens Tech** Win 15-5 786.02 Ignored Mar 1st Garden State 2025
90 Bowdoin Loss 6-12 899.62 Mar 2nd Garden State 2025
196 Haverford Loss 7-8 943.39 Mar 2nd Garden State 2025
411 Boston University-B** Win 12-4 397.87 Ignored Mar 29th Ocean State Invite 2025
322 Central Connecticut State Loss 5-6 470.07 Mar 29th Ocean State Invite 2025
387 Providence Win 8-5 647.73 Mar 29th Ocean State Invite 2025
201 Tufts-B Loss 4-11 447.6 Mar 29th Ocean State Invite 2025
349 Rensselaer Polytech Win 11-6 1001.58 Mar 30th Ocean State Invite 2025
282 Roger Williams Win 11-8 1127.09 Mar 30th Ocean State Invite 2025
349 Rensselaer Polytech Win 11-3 1054.88 Apr 12th Hudson Valley D III Mens Conferences 2025
420 Union (New York)** Win 13-3 -5.75 Ignored Apr 12th Hudson Valley D III Mens Conferences 2025
192 Vassar Win 11-10 1208.23 Apr 12th Hudson Valley D III Mens Conferences 2025
235 Skidmore Loss 10-12 677.08 Apr 13th Hudson Valley D III Mens Conferences 2025
192 Vassar Loss 9-15 567.75 Apr 13th Hudson Valley D III Mens Conferences 2025
315 Hartford Win 15-4 1222.74 Apr 13th Hudson Valley 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)