#7 Carleton College-CUT (15-8)

avg: 2118.64  •  sd: 57.55  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
4 Pittsburgh Loss 11-13 1956.08 Feb 8th Florida Warm Up 2019
69 Emory Win 13-8 2004.62 Feb 8th Florida Warm Up 2019
43 Harvard Win 14-12 1893.24 Feb 8th Florida Warm Up 2019
98 Kansas** Win 13-5 1963.18 Ignored Feb 9th Florida Warm Up 2019
6 Brigham Young Loss 10-13 1806.59 Feb 9th Florida Warm Up 2019
15 Central Florida Loss 8-13 1494.16 Feb 9th Florida Warm Up 2019
20 Tufts Loss 13-15 1649.97 Feb 9th Florida Warm Up 2019
48 Kennesaw State Win 15-4 2246.49 Feb 10th Florida Warm Up 2019
28 Northeastern Win 15-10 2229.43 Feb 10th Florida Warm Up 2019
2 Brown Loss 8-11 1863.55 Mar 2nd Stanford Invite 2019
10 Washington Win 13-11 2273.35 Mar 2nd Stanford Invite 2019
19 Colorado State Win 11-10 2024.55 Mar 2nd Stanford Invite 2019
21 California Win 12-6 2422.77 Mar 3rd Stanford Invite 2019
1 North Carolina Loss 11-12 2106.92 Mar 3rd Stanford Invite 2019
14 Ohio State Win 9-8 2117.06 Mar 3rd Stanford Invite 2019
8 Colorado Loss 8-11 1729.83 Mar 3rd Stanford Invite 2019
43 Harvard Win 13-8 2168.44 Mar 30th Easterns 2019 Men
22 Georgia Win 13-5 2434.49 Mar 30th Easterns 2019 Men
54 Virginia Tech Win 13-7 2176.98 Mar 30th Easterns 2019 Men
1 North Carolina Win 13-11 2460.76 Mar 30th Easterns 2019 Men
2 Brown Loss 12-15 1928.67 Mar 31st Easterns 2019 Men
11 North Carolina State Win 13-8 2523.73 Mar 31st Easterns 2019 Men
3 Oregon Win 15-12 2489.48 Mar 31st Easterns 2019 Men
**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)