#214 MIT (11-11)

avg: 1020.68  •  sd: 48.78  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
223 Colby Win 8-6 1283.21 Feb 22nd Bring The Huckus 2025
179 Dickinson Loss 7-9 886.91 Feb 22nd Bring The Huckus 2025
113 West Chester Loss 7-8 1267.64 Feb 22nd Bring The Huckus 2025
267 SUNY-Geneseo Win 10-9 934.64 Feb 22nd Bring The Huckus 2025
161 Delaware Loss 5-11 630.36 Feb 23rd Bring The Huckus 2025
197 Haverford Loss 8-10 815.8 Feb 23rd Bring The Huckus 2025
235 Skidmore Win 13-12 1052 Feb 23rd Bring The Huckus 2025
230 Harvard Win 7-6 1082.31 Mar 9th MIT Invite
375 Harvard-B** Win 13-1 867.44 Ignored Mar 9th MIT Invite
294 Northeastern-C Loss 8-9 603.13 Mar 9th MIT Invite
364 MIT-B** Win 13-3 968.03 Ignored Mar 9th MIT Invite
133 Bates Win 11-10 1450.44 Mar 22nd PBR State Open
105 Boston University Loss 6-12 870.35 Mar 22nd PBR State Open
162 Brandeis Loss 9-10 1103.22 Mar 22nd PBR State Open
281 Worcester Polytechnic Win 13-7 1321.52 Mar 22nd PBR State Open
348 Bentley Win 15-1 1058.17 Mar 23rd PBR State Open
95 Bowdoin Loss 5-13 880.17 Mar 23rd PBR State Open
105 Boston University Loss 6-15 849.66 Apr 12th Metro Boston D I Mens Conferences 2025
16 Northeastern** Loss 0-15 1456.69 Ignored Apr 12th Metro Boston D I Mens Conferences 2025
85 Boston College Loss 1-15 916.2 Apr 13th Metro Boston D I Mens Conferences 2025
230 Harvard Win 12-11 1082.31 Apr 13th Metro Boston D I Mens Conferences 2025
318 Massachusetts-Lowell Win 15-7 1212.25 Apr 13th Metro Boston D I 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)