#164 Massachusetts -B (8-11)

avg: 1220.7  •  sd: 59.75  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
105 Boston University Loss 6-9 1031.09 Mar 1st UMass Invite 2025
108 Columbia Loss 5-8 989.85 Mar 1st UMass Invite 2025
134 Maine Loss 8-9 1199.57 Mar 1st UMass Invite 2025
76 Williams Loss 1-12 992.52 Mar 1st UMass Invite 2025
108 Columbia Loss 8-9 1318.45 Mar 2nd UMass Invite 2025
206 Tufts-B Win 9-6 1460.88 Mar 2nd UMass Invite 2025
63 Duke Loss 7-10 1259.12 Mar 22nd Atlantic Coast Open 2025
140 George Mason Loss 9-11 1055.71 Mar 22nd Atlantic Coast Open 2025
112 Liberty Loss 10-11 1280.59 Mar 22nd Atlantic Coast Open 2025
264 Virginia Tech-B Win 15-3 1421.24 Mar 22nd Atlantic Coast Open 2025
170 Messiah Loss 13-15 980.88 Mar 23rd Atlantic Coast Open 2025
115 RIT Loss 13-15 1170.04 Mar 23rd Atlantic Coast Open 2025
239 Wake Forest Win 10-4 1519.66 Mar 23rd Atlantic Coast Open 2025
364 MIT-B** Win 15-2 968.03 Ignored Apr 12th New England Dev Mens Conferences 2025
294 Northeastern-C Win 10-5 1302.03 Apr 12th New England Dev Mens Conferences 2025
206 Tufts-B Win 12-7 1562.82 Apr 12th New England Dev Mens Conferences 2025
409 Tufts-C** Win 14-2 438.94 Ignored Apr 12th New England Dev Mens Conferences 2025
294 Northeastern-C Win 10-7 1117.8 Apr 13th New England Dev Mens Conferences 2025
111 Vermont-B Loss 6-9 999.75 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)