#67 Carleton College (12-6)

avg: 1299.76  •  sd: 78.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
145 UCLA-B Win 8-5 1219.89 Feb 8th Stanford Open 2020
163 Sonoma State** Win 13-2 1162.53 Ignored Feb 8th Stanford Open 2020
114 Pacific Lutheran Win 13-2 1600.51 Feb 8th Stanford Open 2020
140 Santa Clara Win 9-6 1220.66 Feb 9th Stanford Open 2020
27 California-Davis Loss 4-5 1620.47 Feb 9th Stanford Open 2020
86 San Diego State University Loss 0-11 574.11 Feb 9th Stanford Open 2020
181 Macalester** Win 13-2 1073.02 Ignored Feb 22nd Forever Winter 2020
80 St Olaf Loss 6-7 1094.07 Feb 22nd Forever Winter 2020
- Michigan Tech Win 9-4 1374.57 Feb 22nd Forever Winter 2020
- Wisconsin-La Crosse** Win 12-2 718.05 Ignored Feb 22nd Forever Winter 2020
208 Nebraska** Win 10-1 809.71 Ignored Mar 7th Midwest Throwdown 2020
223 Wisconsin** Win 12-2 604.43 Ignored Mar 7th Midwest Throwdown 2020
45 Chicago Loss 7-8 1392.53 Mar 7th Midwest Throwdown 2020
22 Northwestern Loss 4-9 1189.74 Mar 7th Midwest Throwdown 2020
117 Iowa State Win 12-5 1559.25 Mar 8th Midwest Throwdown 2020
80 St Olaf Win 7-6 1344.07 Mar 8th Midwest Throwdown 2020
71 Purdue Win 8-7 1406.75 Mar 8th Midwest Throwdown 2020
59 Washington University Loss 5-6 1236.69 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)