#25 Middlebury (16-2)

avg: 2004.07  •  sd: 62.54  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
102 Connecticut Win 12-5 2095.12 Mar 2nd No Sleep till Brooklyn 2024
93 Princeton Win 11-10 1663.03 Mar 2nd No Sleep till Brooklyn 2024
120 Syracuse** Win 13-1 2003.26 Ignored Mar 2nd No Sleep till Brooklyn 2024
141 Boston University** Win 13-3 1942.31 Ignored Mar 3rd No Sleep till Brooklyn 2024
55 Williams Loss 10-12 1511.44 Mar 3rd No Sleep till Brooklyn 2024
75 Dartmouth Win 13-8 2108.65 Mar 16th College Mens Centex Tier 1
107 Iowa State Win 13-9 1894.7 Mar 16th College Mens Centex Tier 1
10 Texas Loss 7-8 2121.19 Mar 16th College Mens Centex Tier 1
49 Michigan State Win 10-7 2168.14 Mar 17th College Mens Centex Tier 1
80 Bates Win 13-11 1819.8 Apr 13th North New England D III Mens Conferences 2024
115 Bowdoin Win 15-5 2036.94 Apr 13th North New England D III Mens Conferences 2024
225 Colby** Win 15-6 1635.69 Ignored Apr 13th North New England D III Mens Conferences 2024
279 Amherst** Win 15-3 1435.51 Ignored May 4th New England D III College Mens Regionals 2024
80 Bates Win 12-7 2111.47 May 4th New England D III College Mens Regionals 2024
225 Colby** Win 15-2 1635.69 Ignored May 4th New England D III College Mens Regionals 2024
218 Middlebury-B** Win 15-6 1654.71 Ignored May 4th New England D III College Mens Regionals 2024
115 Bowdoin Win 13-6 2036.94 May 5th New England D III College Mens Regionals 2024
55 Williams Win 15-9 2265.05 May 5th New England D III College Mens Regionals 2024
**Blowout Eligible

FAQ

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
  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
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)