#123 Denver (13-6)

avg: 821.02  •  sd: 58.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
117 Arizona Win 8-7 1009.59 Jan 28th New Year Fest 2023
150 Arizona State Win 10-9 731.87 Jan 28th New Year Fest 2023
219 Arizona-B** Win 11-4 111.62 Ignored Jan 28th New Year Fest 2023
168 Grand Canyon Win 11-6 991.98 Jan 28th New Year Fest 2023
170 Northern Arizona Win 8-6 710.37 Jan 28th New Year Fest 2023
117 Arizona Win 8-7 1009.59 Jan 29th New Year Fest 2023
170 Northern Arizona Win 10-6 906.04 Jan 29th New Year Fest 2023
117 Arizona Loss 7-10 494.93 Feb 18th Snow Melt 2023
164 Colorado Mines Win 8-6 787.73 Feb 18th Snow Melt 2023
190 Colorado-B Win 9-7 508.52 Feb 18th Snow Melt 2023
91 Colorado College Loss 1-8 455.06 Feb 19th Snow Melt 2023
43 Whitman** Loss 2-11 899 Ignored Feb 19th Snow Melt 2023
73 St. Olaf Loss 2-4 730.73 Apr 1st Illinois Invite1
171 Illinois Win 7-1 1008.98 Apr 1st Illinois Invite1
165 Truman State Win 5-2 1082.35 Apr 1st Illinois Invite1
106 Marquette Loss 6-7 826.74 Apr 1st Illinois Invite1
73 St. Olaf Loss 3-9 626.89 Apr 2nd Illinois Invite1
157 Knox Win 9-6 969.75 Apr 2nd Illinois Invite1
165 Truman State Win 7-1 1082.35 Apr 2nd Illinois Invite1
**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)