#71 Carleton College-CHOP (8-3)

avg: 1120.05  •  sd: 73.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 North Carolina-Charlotte Loss 11-13 745.54 Feb 1st Carolina Kickoff mens 2025
40 North Carolina State Loss 11-13 1175.87 Feb 1st Carolina Kickoff mens 2025
106 Temple Win 13-9 1255.33 Feb 1st Carolina Kickoff mens 2025
126 Appalachian State Win 15-7 1249.8 Feb 2nd Carolina Kickoff mens 2025
50 Case Western Reserve Loss 12-15 1003.4 Feb 2nd Carolina Kickoff mens 2025
106 Temple Win 15-10 1290.37 Feb 2nd Carolina Kickoff mens 2025
158 Minnesota-B Win 12-6 925.75 Feb 8th Gopher Dome 2025
149 Wisconsin-Eau Claire** Win 13-4 1015.55 Ignored Feb 8th Gopher Dome 2025
132 Macalester Win 13-3 1198.21 Feb 8th Gopher Dome 2025
182 Minnesota-C** Win 13-1 508.42 Ignored Feb 8th Gopher Dome 2025
122 St Olaf Win 13-7 1231.02 Feb 8th Gopher Dome 2025
**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)