#17 Colorado State (14-7)

avg: 1869.76  •  sd: 71.85  •  top 16/20: 80%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
100 Arizona Win 12-11 1460.48 Jan 27th Santa Barbara Invitational 2018
148 San Diego State Win 13-6 1747.07 Jan 27th Santa Barbara Invitational 2018
20 Cal Poly-SLO Loss 13-15 1628.94 Jan 27th Santa Barbara Invitational 2018
67 Utah Loss 13-14 1332.97 Jan 27th Santa Barbara Invitational 2018
58 Kansas Win 13-5 2100.86 Jan 28th Santa Barbara Invitational 2018
24 Western Washington Win 13-10 2070.21 Jan 28th Santa Barbara Invitational 2018
15 Stanford Win 13-8 2381.79 Jan 28th Santa Barbara Invitational 2018
5 Washington Loss 7-13 1493.88 Jan 28th Santa Barbara Invitational 2018
47 Iowa State Win 13-7 2125.78 Mar 10th Mens Centex 2018
63 Tulane Win 13-5 2063.68 Mar 10th Mens Centex 2018
26 Texas-Dallas Win 10-7 2118.68 Mar 10th Mens Centex 2018
21 Texas A&M Win 10-7 2211.73 Mar 10th Mens Centex 2018
44 Illinois Win 14-6 2189.03 Mar 11th Mens Centex 2018
31 LSU Loss 10-11 1574.56 Mar 11th Mens Centex 2018
29 Texas Win 12-10 1949.22 Mar 11th Mens Centex 2018
3 Oregon Loss 11-13 1959.93 Mar 24th NW Challenge 2018
38 Southern California Win 13-9 2052.46 Mar 24th NW Challenge 2018
18 Brigham Young Loss 8-12 1412.23 Mar 24th NW Challenge 2018
32 California Loss 9-13 1277.23 Mar 24th NW Challenge 2018
55 Oregon State Win 15-13 1732.36 Mar 25th NW Challenge 2018
32 California Win 15-8 2260.61 Mar 25th NW Challenge 2018
**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)