#19 Colorado State (10-10)

avg: 1899.55  •  sd: 64.6  •  top 16/20: 69%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
90 Santa Clara Win 13-5 1986.86 Jan 26th Santa Barbara Invite 2019
6 Brigham Young Loss 7-13 1577.2 Jan 26th Santa Barbara Invite 2019
42 British Columbia Loss 10-13 1345.46 Jan 26th Santa Barbara Invite 2019
34 UCLA Win 13-6 2328.73 Jan 26th Santa Barbara Invite 2019
21 California Loss 13-14 1718.46 Jan 27th Santa Barbara Invite 2019
34 UCLA Win 12-10 1966.85 Jan 27th Santa Barbara Invite 2019
29 Texas-Dallas Win 10-9 1896.91 Jan 27th Santa Barbara Invite 2019
2 Brown Loss 9-13 1810.59 Mar 2nd Stanford Invite 2019
7 Carleton College-CUT Loss 10-11 1993.64 Mar 2nd Stanford Invite 2019
10 Washington Win 10-9 2169.51 Mar 2nd Stanford Invite 2019
21 California Win 7-3 2443.46 Mar 3rd Stanford Invite 2019
14 Ohio State Loss 10-12 1753.94 Mar 3rd Stanford Invite 2019
30 Victoria Win 13-7 2323.43 Mar 3rd Stanford Invite 2019
27 LSU Loss 11-12 1652.74 Mar 16th Centex 2019 Men
8 Colorado Loss 12-13 1970.44 Mar 16th Centex 2019 Men
29 Texas-Dallas Loss 9-11 1522.7 Mar 16th Centex 2019 Men
76 Utah Win 12-11 1598.73 Mar 16th Centex 2019 Men
13 Wisconsin Loss 13-15 1786.79 Mar 17th Centex 2019 Men
67 Oklahoma State Win 15-10 1987.57 Mar 17th Centex 2019 Men
40 Dartmouth Win 15-8 2251.28 Mar 17th Centex 2019 Men
**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)