#293 Amherst (8-14)

avg: 721.01  •  sd: 50.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
392 Middlebury-B Win 12-0 738.34 Mar 8th Grand Northeast Kickoff 2025
376 New Hampshire Win 8-1 874.79 Mar 8th Grand Northeast Kickoff 2025
253 Brown-B Loss 4-5 732.11 Mar 8th Grand Northeast Kickoff 2025
231 Colby Loss 2-8 327.19 Mar 8th Grand Northeast Kickoff 2025
376 New Hampshire Win 9-3 874.79 Mar 9th Grand Northeast Kickoff 2025
185 Northeastern-B Loss 4-8 551.46 Mar 9th Grand Northeast Kickoff 2025
123 Bates Loss 7-9 1065.16 Mar 9th Grand Northeast Kickoff 2025
370 Wentworth Win 8-7 434.43 Mar 29th New England Open 2025
350 Western New England Win 11-10 570.55 Mar 29th New England Open 2025
351 Clark Win 8-5 875.31 Mar 29th New England Open 2025
250 Worcester Polytechnic Loss 8-9 742.13 Mar 29th New England Open 2025
159 Brandeis Loss 7-13 659.67 Mar 30th New England Open 2025
185 Northeastern-B Loss 9-12 770.91 Mar 30th New England Open 2025
284 Northeastern-C Loss 11-13 528.29 Mar 30th New England Open 2025
351 Clark Win 15-8 986.51 Apr 19th South New England D III Mens Conferences 2025
350 Western New England Win 15-9 961.03 Apr 19th South New England D III Mens Conferences 2025
80 Williams** Loss 3-15 939.38 Ignored Apr 19th South New England D III Mens Conferences 2025
275 Bryant Loss 6-9 358.39 Apr 20th South New England D III Mens Conferences 2025
282 Roger Williams Loss 8-12 320.33 Apr 20th South New England D III Mens Conferences 2025
40 Middlebury Loss 7-13 1216.75 May 3rd New England D III College Mens Regionals 2025
275 Bryant Loss 10-11 651.95 May 3rd New England D III College Mens Regionals 2025
250 Worcester Polytechnic Loss 14-15 742.13 May 4th New England D III 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)