#199 Connecticut College (14-9)

avg: 1125.75  •  sd: 78.05  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
131 Yale Win 10-8 1642.04 Feb 10th UMass Invite 2024
76 Massachusetts -B Loss 9-10 1485.5 Feb 10th UMass Invite 2024
117 Rochester Win 12-5 2027.33 Feb 10th UMass Invite 2024
159 Rhode Island Win 11-9 1537.36 Feb 10th UMass Invite 2024
136 Wesleyan Win 11-10 1493.91 Feb 11th UMass Invite 2024
55 Williams** Loss 5-15 1149.57 Ignored Feb 11th UMass Invite 2024
108 Vermont-B Loss 4-15 875.03 Feb 11th UMass Invite 2024
259 Brandeis Loss 7-10 525.27 Mar 30th New England Open 2024 Open Division
142 Bryant Loss 4-13 740.78 Mar 30th New England Open 2024 Open Division
317 Northeastern-C Win 11-10 780.82 Mar 30th New England Open 2024 Open Division
210 Northeastern-B Loss 7-11 613.77 Mar 30th New England Open 2024 Open Division
343 Connecticut-B Win 13-6 1149.33 Mar 31st New England Open 2024 Open Division
293 Maine Win 11-8 1111.23 Mar 31st New England Open 2024 Open Division
312 Western New England Win 13-4 1273.67 Mar 31st New England Open 2024 Open Division
139 Army Loss 6-10 852.19 Apr 13th Hudson Valley D III Mens Conferences 2024
260 Hartford Loss 9-11 660.4 Apr 13th Hudson Valley D III Mens Conferences 2024
364 Rensselaer Polytech** Win 13-4 1032.24 Ignored Apr 14th Hudson Valley D III Mens Conferences 2024
284 Marist Win 12-11 934.2 Apr 14th Hudson Valley D III Mens Conferences 2024
187 College of New Jersey Win 12-11 1288.42 Apr 27th Metro East D III College Mens Regionals 2024
364 Rensselaer Polytech** Win 15-1 1032.24 Ignored Apr 27th Metro East D III College Mens Regionals 2024
252 Hamilton Win 15-8 1495.65 Apr 27th Metro East D III College Mens Regionals 2024
213 Ithaca Win 15-9 1591.12 Apr 28th Metro East D III College Mens Regionals 2024
117 Rochester Loss 7-10 1037.67 Apr 28th Metro East 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)