#343 Connecticut-B (8-10)

avg: 549.33  •  sd: 60.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
188 Brown-B** Loss 0-4 563.41 Ignored Mar 2nd Philly Special 2024
310 Stevens Tech Loss 6-7 553.83 Mar 2nd Philly Special 2024
397 SUNY-Albany-B Win 6-0 692.79 Mar 2nd Philly Special 2024
359 Bentley Win 14-7 1036.85 Mar 3rd Philly Special 2024
401 Siena Win 15-3 639.98 Mar 3rd Philly Special 2024
397 SUNY-Albany-B Win 15-4 692.79 Mar 3rd Philly Special 2024
138 Tufts-B Loss 5-11 763.74 Mar 30th New England Open 2024 Open Division
210 Northeastern-B Loss 1-13 480.66 Mar 30th New England Open 2024 Open Division
312 Western New England Loss 6-10 177.51 Mar 30th New England Open 2024 Open Division
317 Northeastern-C Loss 7-10 266.15 Mar 30th New England Open 2024 Open Division
199 Connecticut College Loss 6-13 525.75 Mar 31st New England Open 2024 Open Division
329 Harvard-B Win 9-7 879.5 Mar 31st New England Open 2024 Open Division
317 Northeastern-C Loss 5-6 530.82 Mar 31st New England Open 2024 Open Division
411 RIT-B Win 15-7 334 Apr 13th Metro East Dev Mens Conferences 2024
331 Rutgers-B Loss 6-12 16.97 Apr 13th Metro East Dev Mens Conferences 2024
378 SUNY-Buffalo-B Win 14-8 837.73 Apr 14th Metro East Dev Mens Conferences 2024
327 SUNY-Binghamton-B Loss 9-11 365.01 Apr 14th Metro East Dev Mens Conferences 2024
345 Cornell-B Win 10-8 796.77 Apr 14th Metro East Dev Mens Conferences 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)