#206 Tufts-B (10-7)

avg: 1042.31  •  sd: 102.38  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
123 Connecticut Loss 8-11 994.83 Mar 1st UMass Invite 2025
36 Middlebury** Loss 3-13 1218.97 Ignored Mar 1st UMass Invite 2025
111 Vermont-B Win 10-9 1543.32 Mar 1st UMass Invite 2025
75 Wesleyan Win 9-6 2017.93 Mar 1st UMass Invite 2025
164 Massachusetts -B Loss 6-9 802.13 Mar 2nd UMass Invite 2025
411 Boston University-B** Win 13-2 389.91 Ignored Mar 29th Ocean State Invite 2025
325 Central Connecticut State Win 13-2 1197.71 Mar 29th Ocean State Invite 2025
247 Connecticut College Win 11-4 1489.56 Mar 29th Ocean State Invite 2025
387 Providence** Win 13-2 792.91 Ignored Mar 29th Ocean State Invite 2025
325 Central Connecticut State Win 15-4 1197.71 Mar 30th Ocean State Invite 2025
152 Rhode Island Loss 8-12 827.27 Mar 30th Ocean State Invite 2025
364 MIT-B** Win 10-4 968.03 Ignored Apr 12th New England Dev Mens Conferences 2025
294 Northeastern-C Loss 9-10 603.13 Apr 12th New England Dev Mens Conferences 2025
409 Tufts-C** Win 12-2 438.94 Ignored Apr 12th New England Dev Mens Conferences 2025
164 Massachusetts -B Loss 7-12 700.19 Apr 12th New England Dev Mens Conferences 2025
262 Brown-B Loss 7-9 549.66 Apr 13th New England Dev Mens Conferences 2025
375 Harvard-B** Win 12-4 867.44 Ignored Apr 13th New England Dev Mens Conferences 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)