#1 Carleton College (11-2)

avg: 2459.36  •  sd: 83.46  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
27 California-Davis** Win 15-6 2261.5 Ignored Jan 27th Santa Barbara Invite 2024
39 Cal Poly-SLO** Win 15-4 2098.43 Ignored Jan 27th Santa Barbara Invite 2024
8 Washington Win 14-9 2606.37 Jan 27th Santa Barbara Invite 2024
7 Stanford Win 12-8 2609 Jan 28th Santa Barbara Invite 2024
13 California-Santa Cruz Win 15-10 2355.75 Jan 28th Santa Barbara Invite 2024
3 British Columbia Loss 12-13 2266.97 Jan 28th Santa Barbara Invite 2024
71 Appalachian State** Win 15-1 1686.4 Ignored Feb 10th Queen City Tune Up 2024
16 Notre Dame Win 15-6 2487.53 Feb 10th Queen City Tune Up 2024
24 Pittsburgh** Win 13-5 2302.98 Ignored Feb 10th Queen City Tune Up 2024
21 Washington University** Win 15-0 2350.09 Ignored Feb 10th Queen City Tune Up 2024
2 North Carolina Loss 11-15 2022.48 Feb 11th Queen City Tune Up 2024
9 Michigan Win 11-5 2658.65 Feb 11th Queen City Tune Up 2024
4 Vermont Win 15-11 2684.6 Feb 11th Queen City Tune Up 2024
**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)