#84 Boston College (14-11)

avg: 1507.56  •  sd: 53.34  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
189 Baylor Win 11-5 1691.24 Mar 15th Mens Centex 2025
183 Tarleton State Loss 10-11 1007.45 Mar 15th Mens Centex 2025
219 Texas-B Win 12-2 1585.24 Mar 15th Mens Centex 2025
296 Trinity** Win 13-3 1311.59 Ignored Mar 15th Mens Centex 2025
224 Arkansas Win 15-10 1413.43 Mar 16th Mens Centex 2025
92 Texas A&M Loss 12-14 1256.92 Mar 16th Mens Centex 2025
288 Harding** Win 15-5 1345.34 Ignored Mar 16th Mens Centex 2025
51 Cornell Loss 10-11 1581.16 Mar 29th East Coast Invite 2025
74 Temple Loss 10-12 1345.94 Mar 29th East Coast Invite 2025
155 Johns Hopkins Win 9-7 1517.85 Mar 29th East Coast Invite 2025
98 SUNY-Buffalo Loss 10-11 1332.75 Mar 29th East Coast Invite 2025
64 Georgetown Win 15-8 2193.21 Mar 30th East Coast Invite 2025
113 West Chester Loss 10-11 1250.96 Mar 30th East Coast Invite 2025
93 Yale Win 12-4 2076.76 Mar 30th East Coast Invite 2025
229 Harvard Win 14-7 1521.92 Apr 12th Metro Boston D I Mens Conferences 2025
20 Tufts Loss 6-13 1395.17 Apr 12th Metro Boston D I Mens Conferences 2025
316 Massachusetts-Lowell** Win 14-3 1219.5 Ignored Apr 12th Metro Boston D I Mens Conferences 2025
222 MIT Win 15-1 1576.13 Apr 13th Metro Boston D I Mens Conferences 2025
116 Boston University Loss 12-14 1142.66 Apr 13th Metro Boston D I Mens Conferences 2025
18 Brown Loss 4-15 1412.59 May 3rd New England D I College Mens Regionals 2025
284 Northeastern-C Win 15-8 1321.94 May 3rd New England D I College Mens Regionals 2025
125 Maine Win 14-10 1738.96 May 3rd New England D I College Mens Regionals 2025
2 Massachusetts** Loss 3-15 1769.51 Ignored May 3rd New England D I College Mens Regionals 2025
39 McGill Loss 12-13 1653.63 May 4th New England D I College Mens Regionals 2025
91 Vermont-B Win 13-12 1603.29 May 4th New England D I College Mens Regionals 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)