#53 Colorado State (9-11)

avg: 1470.56  •  sd: 63.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
151 Cal Poly-SLO-B Win 15-7 1633.94 Jan 27th Santa Barbara Invite 2024
15 California Loss 6-15 1324.22 Jan 27th Santa Barbara Invite 2024
54 California-Santa Barbara Win 15-7 2069.64 Jan 27th Santa Barbara Invite 2024
30 Utah Loss 7-14 1094.11 Jan 27th Santa Barbara Invite 2024
24 British Columbia Loss 6-13 1200.53 Jan 28th Santa Barbara Invite 2024
43 California-San Diego Loss 12-14 1341.31 Jan 28th Santa Barbara Invite 2024
35 California-Santa Cruz Loss 11-13 1408.37 Jan 28th Santa Barbara Invite 2024
17 Brigham Young Loss 10-13 1547.3 Mar 15th College Mens Centex Tier 1
47 Oklahoma Christian Loss 9-12 1174.81 Mar 16th College Mens Centex Tier 1
48 Missouri Loss 10-12 1276.65 Mar 16th College Mens Centex Tier 1
44 Tulane Win 8-7 1666.68 Mar 16th College Mens Centex Tier 1
20 Northeastern Loss 8-13 1334.16 Mar 17th College Mens Centex Tier 1
121 Iowa State Loss 10-11 1030.06 Mar 17th College Mens Centex Tier 1
132 Arkansas Win 9-5 1640.91 Mar 30th Huck Finn 2024
88 Kentucky Win 10-8 1565.09 Mar 30th Huck Finn 2024
65 Stanford Win 9-5 1933.99 Mar 30th Huck Finn 2024
91 Indiana Loss 9-10 1145.81 Mar 30th Huck Finn 2024
76 Purdue Win 12-7 1877.73 Mar 31st Huck Finn 2024
118 Michigan Tech Win 13-7 1731.16 Mar 31st Huck Finn 2024
108 Wisconsin-Milwaukee Win 7-6 1324.7 Mar 31st Huck Finn 2024
**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)