#69 Carleton College-GoP (15-11)

avg: 1449.46  •  sd: 64.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
1 North Carolina Loss 7-13 1787.8 Jan 20th Carolina Kickoff 2018 NC Ultimate
16 North Carolina-Wilmington Loss 7-13 1326.98 Jan 20th Carolina Kickoff 2018 NC Ultimate
91 Penn State Loss 6-13 773.9 Jan 20th Carolina Kickoff 2018 NC Ultimate
12 North Carolina State Loss 5-13 1318.86 Jan 21st Carolina Kickoff 2018 NC Ultimate
37 Central Florida Win 10-8 1897.42 Jan 21st Carolina Kickoff 2018 NC Ultimate
14 Florida Loss 9-13 1468.25 Jan 21st Carolina Kickoff 2018 NC Ultimate
263 Sacramento State** Win 13-4 1341.99 Ignored Feb 10th Stanford Open 2018
397 California-Santa Barbara-B** Win 13-1 707.16 Ignored Feb 10th Stanford Open 2018
53 UCLA Win 11-10 1659.42 Feb 10th Stanford Open 2018
87 Las Positas Loss 7-11 926.31 Feb 11th Stanford Open 2018
59 Santa Clara Win 12-11 1624.77 Feb 11th Stanford Open 2018
129 Claremont Win 12-11 1320.86 Feb 11th Stanford Open 2018
85 Colorado College Win 10-9 1524.36 Feb 11th Stanford Open 2018
45 Illinois State Loss 10-15 1132.55 Mar 3rd Midwest Throwdown 2018
51 Ohio State Loss 13-15 1323.51 Mar 3rd Midwest Throwdown 2018
164 St John's Win 15-8 1638.21 Mar 3rd Midwest Throwdown 2018
99 Missouri S&T Win 15-11 1719.05 Mar 4th Midwest Throwdown 2018
171 Truman State Win 15-2 1638.6 Mar 4th Midwest Throwdown 2018
74 Washington University Loss 10-11 1293.49 Mar 4th Midwest Throwdown 2018
248 North Georgia** Win 12-5 1374.33 Ignored Mar 17th College Southerns 2018
207 Florida-B Win 13-5 1520.75 Mar 17th College Southerns 2018
224 Georgia Southern Win 13-6 1461.24 Mar 17th College Southerns 2018
75 Tennessee-Chattanooga Loss 11-12 1290.67 Mar 17th College Southerns 2018
150 North Carolina-Asheville Win 13-6 1731.08 Mar 18th College Southerns 2018
76 Chicago Loss 6-12 836 Mar 18th College Southerns 2018
75 Tennessee-Chattanooga Win 13-5 2015.67 Mar 18th College Southerns 2018
**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)