#143 Colorado-B (10-7)

avg: 1182.73  •  sd: 80.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
135 Brigham Young-B Loss 10-12 991.05 Jan 28th New Year Fest 2023
229 Arizona Win 13-11 1025.86 Jan 28th New Year Fest 2023
356 Denver-B** Win 13-2 537.71 Ignored Jan 28th New Year Fest 2023
55 Utah Valley Loss 10-13 1286.04 Jan 28th New Year Fest 2023
117 Colorado Mines Loss 5-12 704.9 Jan 29th New Year Fest 2023
184 Denver Win 10-6 1504.14 Jan 29th New Year Fest 2023
40 Colorado College Loss 6-10 1239.19 Feb 18th Snow Melt 2023
- Colorado State-B** Win 11-1 785.03 Ignored Feb 18th Snow Melt 2023
356 Denver-B** Win 11-1 537.71 Ignored Feb 18th Snow Melt 2023
117 Colorado Mines Loss 7-9 1025.57 Feb 19th Snow Melt 2023
184 Denver Win 11-10 1132.98 Feb 19th Snow Melt 2023
249 Texas-San Antonio Win 13-5 1336.79 Mar 11th Centex Tier 2
48 Iowa State Loss 5-13 1046.34 Mar 11th Centex Tier 2
96 Arkansas Loss 8-15 856.68 Mar 12th Centex Tier 2
271 Texas A&M-B Win 15-6 1232.68 Mar 12th Centex Tier 2
161 Rice Win 15-10 1565.41 Mar 12th Centex Tier 2
193 North Texas Win 7-3 1574.85 Mar 12th Centex Tier 2
**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)