#7 Carleton College (6-1)

avg: 1944.1  •  sd: 77.9  •  top 16/20: 99.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
46 Florida Win 13-11 1658.69 Feb 2nd Florida Warm Up 2024
9 Brown Win 12-11 2011.17 Feb 2nd Florida Warm Up 2024
50 Cornell Win 13-8 1878.66 Feb 2nd Florida Warm Up 2024
14 Minnesota Loss 14-15 1687.19 Feb 3rd Florida Warm Up 2024
2 Georgia Win 11-10 2256.77 Feb 3rd Florida Warm Up 2024
30 Washington University Win 13-7 2151.2 Feb 3rd Florida Warm Up 2024
13 Northeastern Win 15-14 1965.04 Feb 4th Florida Warm Up 2024
**Blowout Eligible


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)