#229 Carleton College Karls (4-6)

avg: 737.88  •  sd: 56.94  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
- Minnesota-C Loss 7-12 667.68 Feb 15th When in Dome
- St Olaf** Loss 5-13 893.18 Feb 15th When in Dome
- Macalester Loss 10-12 652.78 Feb 15th When in Dome
108 Chicago Loss 1-13 582.47 Mar 7th Midwest Throwdown 2020
100 Truman State Loss 5-12 609.73 Mar 7th Midwest Throwdown 2020
164 Illinois State Win 11-10 1098.7 Mar 7th Midwest Throwdown 2020
353 UW-Eau Claire-B** Win 12-1 466.57 Ignored Mar 7th Midwest Throwdown 2020
256 Northern Iowa Win 8-7 723.55 Mar 8th Midwest Throwdown 2020
231 Northwestern-B Win 8-7 844.29 Mar 8th Midwest Throwdown 2020
196 Wisconsin-Eau Claire Loss 7-9 558.92 Mar 8th Midwest Throwdown 2020
**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)