#35 Middlebury (18-2)

avg: 1726.5  •  sd: 46.39  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
146 North Carolina-Asheville Win 13-10 1516.31 Mar 2nd FCS D III Tune Up 2019
173 Georgia College Win 13-7 1626.64 Mar 2nd FCS D III Tune Up 2019
113 Davidson Win 13-6 1901.9 Mar 2nd FCS D III Tune Up 2019
75 Air Force Win 14-13 1602.54 Mar 2nd FCS D III Tune Up 2019
247 Xavier Win 13-7 1432.28 Mar 3rd FCS D III Tune Up 2019
91 Mary Washington Win 13-11 1611.35 Mar 3rd FCS D III Tune Up 2019
246 Florida-B** Win 13-3 1475.42 Ignored Mar 23rd College Southerns XVIII
240 Wisconsin-Eau Claire** Win 13-5 1489.84 Ignored Mar 23rd College Southerns XVIII
234 Florida Tech** Win 13-5 1506.26 Ignored Mar 23rd College Southerns XVIII
78 Carleton College-GoP Win 13-8 1953.88 Mar 23rd College Southerns XVIII
25 South Carolina Loss 11-15 1405.53 Mar 24th College Southerns XVIII
136 South Florida Win 15-5 1837.03 Mar 24th College Southerns XVIII
94 Appalachian State Win 15-8 1937.24 Mar 24th College Southerns XVIII
66 Penn State Win 12-10 1773.37 Mar 30th Atlantic Coast Open 2019
62 Duke Loss 11-12 1426 Mar 30th Atlantic Coast Open 2019
102 Georgetown Win 13-6 1951.18 Mar 30th Atlantic Coast Open 2019
158 Lehigh Win 13-9 1547.64 Mar 30th Atlantic Coast Open 2019
33 Johns Hopkins Win 12-10 1969.29 Mar 31st Atlantic Coast Open 2019
91 Mary Washington Win 13-9 1801.08 Mar 31st Atlantic Coast Open 2019
62 Duke Win 15-11 1932.17 Mar 31st Atlantic Coast Open 2019
**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)