#46 Middlebury (8-3)

avg: 1538.47  •  sd: 92.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
254 Georgia Tech-B** Win 12-4 715.46 Ignored Mar 23rd College Southerns XVIII
149 Luther** Win 13-2 1469.27 Ignored Mar 23rd College Southerns XVIII
49 Emory Win 11-10 1643.42 Mar 23rd College Southerns XVIII
20 North Carolina-Wilmington Loss 10-11 1835.18 Mar 23rd College Southerns XVIII
122 Georgia College Win 14-10 1421.92 Mar 24th College Southerns XVIII
64 Carleton College-Eclipse Win 10-9 1530.19 Mar 24th College Southerns XVIII
26 Georgia Loss 6-13 1248.3 Mar 30th I 85 Rodeo 2019
82 Georgetown Win 9-8 1391.46 Mar 30th I 85 Rodeo 2019
166 Richmond Win 13-7 1328.01 Mar 30th I 85 Rodeo 2019
18 South Carolina Loss 9-10 1846.42 Mar 31st I 85 Rodeo 2019
85 Dayton Win 11-8 1608.92 Mar 31st I 85 Rodeo 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)