#10 Carleton College-CUT (11-5)

avg: 2033.63  •  sd: 66.34  •  top 16/20: 99.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
8 Massachusetts Win 13-10 2388.4 Feb 14th Florida Warm Up 2020 Weekend 1
13 Brown Win 11-9 2177.26 Feb 14th Florida Warm Up 2020 Weekend 1
99 Central Florida Win 13-7 1769.21 Feb 14th Florida Warm Up 2020 Weekend 1
47 Auburn Win 13-7 2085.15 Feb 15th Florida Warm Up 2020 Weekend 1
35 Northeastern Win 13-10 1959.5 Feb 15th Florida Warm Up 2020 Weekend 1
9 Pittsburgh Win 15-10 2496.36 Feb 15th Florida Warm Up 2020 Weekend 1
3 Brigham Young Loss 9-10 2054.16 Feb 15th Florida Warm Up 2020 Weekend 1
17 Michigan Loss 11-12 1722.66 Feb 16th Florida Warm Up 2020 Weekend 1
25 Georgia Tech Win 13-10 2104.89 Feb 16th Florida Warm Up 2020 Weekend 1
9 Pittsburgh Loss 12-15 1742.26 Mar 7th Smoky Mountain Invite 2020
21 North Carolina State Win 11-6 2367.61 Mar 7th Smoky Mountain Invite 2020
31 Texas-Dallas Loss 10-11 1572.27 Mar 7th Smoky Mountain Invite 2020
7 Ohio State Loss 8-11 1703.59 Mar 7th Smoky Mountain Invite 2020
29 Wisconsin Win 15-9 2220.43 Mar 8th Smoky Mountain Invite 2020
30 Texas Win 15-9 2213.91 Mar 8th Smoky Mountain Invite 2020
22 Georgia Win 13-11 2045.79 Mar 8th Smoky Mountain Invite 2020
**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)