#78 Carleton College-GoP (10-7)

avg: 1457.72  •  sd: 58.96  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
85 Richmond Win 13-9 1848.27 Jan 26th Carolina Kickoff 2019
62 Duke Win 13-10 1879.15 Jan 26th Carolina Kickoff 2019
1 North Carolina** Loss 2-13 1631.92 Ignored Jan 26th Carolina Kickoff 2019
55 Florida State Loss 12-15 1311.18 Jan 27th Carolina Kickoff 2019
11 North Carolina State Loss 5-15 1427.57 Jan 27th Carolina Kickoff 2019
81 Georgia Tech Loss 9-13 1028.75 Jan 27th Carolina Kickoff 2019
- Sacramento State** Win 13-2 1150.62 Ignored Feb 9th Stanford Open 2019
- Oregon-B** Win 13-5 1361.58 Ignored Feb 9th Stanford Open 2019
199 Claremont Win 12-6 1575.65 Feb 9th Stanford Open 2019
116 Nevada-Reno Loss 7-8 1168.72 Feb 10th Stanford Open 2019
180 Humboldt State Win 8-3 1658.43 Feb 10th Stanford Open 2019
35 Middlebury Loss 8-13 1230.34 Mar 23rd College Southerns XVIII
234 Florida Tech Win 13-4 1506.26 Mar 23rd College Southerns XVIII
240 Wisconsin-Eau Claire Win 13-2 1489.84 Mar 23rd College Southerns XVIII
246 Florida-B Win 13-6 1475.42 Mar 23rd College Southerns XVIII
25 South Carolina Loss 7-15 1186.69 Mar 24th College Southerns XVIII
56 California-San Diego Win 13-12 1717.76 Mar 24th College Southerns XVIII
**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)