#138 Tufts-B (17-5)

avg: 1363.74  •  sd: 60.57  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
279 Amherst Win 15-9 1350.99 Mar 2nd Grand Northeast Kickoff
225 Colby Win 15-9 1551.17 Mar 2nd Grand Northeast Kickoff
218 Middlebury-B Win 15-7 1654.71 Mar 2nd Grand Northeast Kickoff
76 Massachusetts -B Loss 7-15 1010.5 Mar 3rd Grand Northeast Kickoff
76 Massachusetts -B Loss 8-15 1045.69 Mar 3rd Grand Northeast Kickoff
312 Western New England Win 15-8 1238.48 Mar 3rd Grand Northeast Kickoff
343 Connecticut-B Win 11-5 1149.33 Mar 30th New England Open 2024 Open Division
210 Northeastern-B Win 8-5 1534.26 Mar 30th New England Open 2024 Open Division
312 Western New England Win 10-5 1247.57 Mar 30th New England Open 2024 Open Division
259 Brandeis Win 10-7 1304.6 Mar 31st New England Open 2024 Open Division
225 Colby Win 9-6 1454.25 Mar 31st New England Open 2024 Open Division
189 Worcester Polytechnic Institute Loss 7-8 1034.5 Mar 31st New England Open 2024 Open Division
329 Harvard-B Win 10-5 1174.06 Apr 13th Metro Boston Dev Mens Conferences 2024
210 Northeastern-B Win 10-5 1654.56 Apr 13th Metro Boston Dev Mens Conferences 2024
317 Northeastern-C** Win 13-5 1255.82 Ignored Apr 13th Metro Boston Dev Mens Conferences 2024
377 MIT-B** Win 13-2 907.62 Ignored Apr 14th Metro Boston Dev Mens Conferences 2024
210 Northeastern-B Win 9-6 1499.23 Apr 14th Metro Boston Dev Mens Conferences 2024
398 Tufts-C** Win 7-0 684.69 Ignored Apr 14th Metro Boston Dev Mens Conferences 2024
4 Massachusetts** Loss 3-13 1854.1 Ignored May 4th New England D I College Mens Regionals 2024
76 Massachusetts -B Loss 9-10 1485.5 May 4th New England D I College Mens Regionals 2024
108 Vermont-B Win 13-11 1703.87 May 4th New England D I College Mens Regionals 2024
201 MIT Win 12-11 1241.71 May 5th New England D I College Mens Regionals 2024
**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)