#289 Connecticut-B (5-8)

avg: 410.73  •  sd: 59.84  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
143 Brown-B** Loss 0-4 465.97 Ignored Mar 2nd Philly Special 2024
277 Stevens Tech Loss 6-7 372.45 Mar 2nd Philly Special 2024
357 SUNY-Albany-B Win 6-0 583.16 Mar 2nd Philly Special 2024
315 Bentley Win 14-7 864.87 Mar 3rd Philly Special 2024
367 Siena Win 15-3 466.38 Mar 3rd Philly Special 2024
357 SUNY-Albany-B Win 15-4 583.16 Mar 3rd Philly Special 2024
138 Tufts-B Loss 5-11 484.84 Mar 30th New England Open 2024 Open Division
181 Northeastern-B Loss 1-13 298.07 Mar 30th New England Open 2024 Open Division
269 Western New England Loss 6-10 16.17 Mar 30th New England Open 2024 Open Division
272 Northeastern-C Loss 7-10 118.31 Mar 30th New England Open 2024 Open Division
183 Connecticut College Loss 6-13 288.7 Mar 31st New England Open 2024 Open Division
295 Harvard-B Win 9-7 648.92 Mar 31st New England Open 2024 Open Division
272 Northeastern-C Loss 5-6 382.98 Mar 31st New England Open 2024 Open Division
**Blowout Eligible

FAQ

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
  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
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)