#40 Colorado College (12-5)

avg: 1735.34  •  sd: 118.83  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
356 Denver-B** Win 11-0 537.71 Ignored Feb 18th Snow Melt 2023
- Colorado State-B** Win 11-3 785.03 Ignored Feb 18th Snow Melt 2023
143 Colorado-B Win 10-6 1678.89 Feb 18th Snow Melt 2023
117 Colorado Mines Win 13-6 1904.9 Feb 19th Snow Melt 2023
279 Wisconsin-Platteville** Win 13-4 1185.54 Ignored Mar 4th Midwest Throwdown 2023
334 Northwestern-B** Win 13-2 837.28 Ignored Mar 4th Midwest Throwdown 2023
94 Saint Louis Win 11-8 1790.41 Mar 4th Midwest Throwdown 2023
92 Missouri S&T Win 13-8 1925.97 Mar 5th Midwest Throwdown 2023
64 St. Olaf Win 13-4 2168 Mar 5th Midwest Throwdown 2023
22 Washington University Loss 9-10 1780.32 Mar 5th Midwest Throwdown 2023
26 Georgia Tech Loss 10-12 1630.21 Mar 18th Centex 2023
28 Oklahoma Christian Loss 11-12 1718.49 Mar 18th Centex 2023
86 Dartmouth Win 12-9 1782.33 Mar 18th Centex 2023
47 Colorado State Win 10-8 1909.89 Mar 18th Centex 2023
60 Middlebury Win 11-10 1703.03 Mar 19th Centex 2023
26 Georgia Tech Loss 5-15 1268.33 Mar 19th Centex 2023
54 Northwestern Loss 13-15 1402.01 Mar 19th Centex 2023
**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)