#3 Carleton College (15-4)

avg: 2706.55  •  sd: 79.28  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
23 Cal Poly-SLO** Win 15-4 2571.29 Ignored Jan 27th Santa Barbara Invite 2024
24 California-Davis** Win 15-6 2571.09 Ignored Jan 27th Santa Barbara Invite 2024
10 Washington Win 14-9 2822.65 Jan 27th Santa Barbara Invite 2024
6 Stanford Win 12-8 2999.78 Jan 28th Santa Barbara Invite 2024
1 British Columbia Loss 12-13 2769.15 Jan 28th Santa Barbara Invite 2024
14 California-Santa Cruz Win 15-10 2633.84 Jan 28th Santa Barbara Invite 2024
97 Appalachian State** Win 15-1 1820.34 Ignored Feb 10th Queen City Tune Up 2024
22 Notre Dame** Win 15-6 2655.17 Ignored Feb 10th Queen City Tune Up 2024
25 Pittsburgh** Win 13-5 2562.92 Ignored Feb 10th Queen City Tune Up 2024
37 Washington University** Win 15-0 2368.65 Ignored Feb 10th Queen City Tune Up 2024
12 Michigan Win 11-5 2911.61 Feb 11th Queen City Tune Up 2024
2 Vermont Win 15-11 3170.79 Feb 11th Queen City Tune Up 2024
4 North Carolina Loss 11-15 2241.24 Feb 11th Queen City Tune Up 2024
30 California Win 13-7 2442.08 Mar 16th NW Challenge 2024
44 Whitman** Win 13-2 2292.34 Ignored Mar 16th NW Challenge 2024
8 Colorado Loss 12-13 2333.07 Mar 16th NW Challenge 2024
1 British Columbia Loss 8-13 2397.99 Mar 17th NW Challenge 2024
5 Oregon Win 13-6 3205.7 Mar 17th NW Challenge 2024
4 North Carolina Win 13-12 2747.41 Mar 17th NW Challenge 2024
**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)