#3 Carleton College (8-2)

avg: 2063.74  •  sd: 90.24  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
10 California-San Diego Win 12-8 2122.79 Jan 25th Santa Barbara Invite 2025
27 UCLA** Win 13-5 1919.44 Ignored Jan 25th Santa Barbara Invite 2025
9 Cal Poly-SLO Win 12-8 2133.64 Jan 26th Santa Barbara Invite 2025
5 Colorado Win 10-4 2401.8 Jan 26th Santa Barbara Invite 2025
4 Oregon Loss 8-10 1627.86 Jan 26th Santa Barbara Invite 2025
53 Florida** Win 13-0 1584.02 Ignored Feb 15th Queen City Tune Up 2025
7 Northeastern Win 13-1 2306.36 Feb 15th Queen City Tune Up 2025
38 South Carolina** Win 13-1 1696.49 Ignored Feb 15th Queen City Tune Up 2025
2 Tufts Loss 7-9 1826.35 Feb 16th Queen City Tune Up 2025
17 Vermont Win 11-6 2037.57 Feb 16th Queen City Tune Up 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)