#237 Connecticut College (7-4)

avg: 649.5  •  sd: 60.05  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
347 Army Win 12-4 718.29 Mar 1st Garden State 2025
330 Cornell-B Win 14-4 832.58 Mar 1st Garden State 2025
362 Stevens Tech** Win 15-5 583.55 Ignored Mar 1st Garden State 2025
112 Bowdoin Loss 6-12 625.27 Mar 2nd Garden State 2025
217 Haverford Loss 7-8 616.57 Mar 2nd Garden State 2025
383 Boston University-B** Win 12-4 292.91 Ignored Mar 29th Ocean State Invite 2025
278 Central Connecticut State Loss 5-6 344.54 Mar 29th Ocean State Invite 2025
358 Providence Win 8-5 469.18 Mar 29th Ocean State Invite 2025
152 Tufts-B Loss 4-11 446.42 Mar 29th Ocean State Invite 2025
301 Rensselaer Polytech Win 11-6 922.75 Mar 30th Ocean State Invite 2025
283 Roger Williams Win 11-8 802.84 Mar 30th Ocean State Invite 2025
**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)