#119 Connecticut (18-10)

avg: 1350.18  •  sd: 56.62  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
106 Columbia Win 13-8 1925.01 Feb 8th NJ Warmup 2025
248 NYU Win 11-5 1475.07 Feb 8th NJ Warmup 2025
100 Syracuse Win 11-10 1578.35 Feb 8th NJ Warmup 2025
166 Pennsylvania Win 9-8 1319.08 Feb 8th NJ Warmup 2025
201 Tufts-B Win 11-8 1413.21 Mar 1st UMass Invite 2025
40 Middlebury Loss 7-10 1384.62 Mar 1st UMass Invite 2025
91 Vermont-B Loss 7-9 1198.96 Mar 1st UMass Invite 2025
73 Wesleyan Win 7-6 1712.29 Mar 1st UMass Invite 2025
125 Maine Win 8-5 1793.86 Mar 2nd UMass Invite 2025
40 Middlebury Loss 8-12 1333.13 Mar 2nd UMass Invite 2025
91 Vermont-B Loss 4-7 982.14 Mar 2nd UMass Invite 2025
74 Temple Loss 9-11 1334.86 Mar 29th East Coast Invite 2025
100 Syracuse Loss 8-12 1012.2 Mar 29th East Coast Invite 2025
229 Harvard Win 12-5 1539.03 Mar 29th East Coast Invite 2025
52 William & Mary Loss 6-11 1146.85 Mar 29th East Coast Invite 2025
106 Columbia Loss 5-7 1100.71 Mar 30th East Coast Invite 2025
208 Towson Win 11-10 1149.8 Mar 30th East Coast Invite 2025
149 Rutgers Win 9-7 1542.97 Mar 30th East Coast Invite 2025
322 Central Connecticut State Win 9-7 874.41 Apr 12th Hudson Valley D I Mens Conferences 2025
213 SUNY-Albany Win 10-8 1275.08 Apr 12th Hudson Valley D I Mens Conferences 2025
382 Southern Connecticut State** Win 13-2 839.83 Ignored Apr 12th Hudson Valley D I Mens Conferences 2025
93 Yale Win 12-4 2076.76 Apr 12th Hudson Valley D I Mens Conferences 2025
106 Columbia Loss 10-11 1303.85 Apr 26th Metro East D I College Mens Regionals 2025
213 SUNY-Albany Win 14-13 1137.42 Apr 26th Metro East D I College Mens Regionals 2025
331 Hofstra** Win 15-0 1170.89 Ignored Apr 26th Metro East D I College Mens Regionals 2025
248 NYU Win 13-8 1371.22 Apr 26th Metro East D I College Mens Regionals 2025
100 Syracuse Loss 9-14 979.48 Apr 27th Metro East D I College Mens Regionals 2025
115 RIT Win 15-13 1581.18 Apr 27th Metro East D I 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)