#19 Colorado (12-8)

avg: 1850.95  •  sd: 67.39  •  top 16/20: 70.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
20 Cal Poly-SLO Win 12-10 2081.24 Feb 17th Presidents Day Invitational Tournament 2018
76 Chicago Win 15-2 2015.31 Feb 17th Presidents Day Invitational Tournament 2018
65 California-Santa Barbara Win 13-12 1587.37 Feb 17th Presidents Day Invitational Tournament 2018
38 Southern California Win 11-7 2100.79 Feb 18th Presidents Day Invitational Tournament 2018
32 California Win 12-11 1820.8 Feb 18th Presidents Day Invitational Tournament 2018
67 Utah Win 14-6 2057.97 Feb 18th Presidents Day Invitational Tournament 2018
20 Cal Poly-SLO Loss 9-11 1593.91 Feb 19th Presidents Day Invitational Tournament 2018
32 California Loss 7-10 1306.13 Feb 19th Presidents Day Invitational Tournament 2018
3 Oregon Loss 5-13 1588.77 Mar 3rd Stanford Invite 2018
22 Tufts Win 11-9 1999.38 Mar 3rd Stanford Invite 2018
13 Wisconsin Loss 8-11 1551.51 Mar 3rd Stanford Invite 2018
7 Pittsburgh Loss 10-13 1659.32 Mar 4th Stanford Invite 2018
55 Oregon State Win 11-9 1767.38 Mar 4th Stanford Invite 2018
15 Stanford Win 13-7 2443.16 Mar 4th Stanford Invite 2018
18 Brigham Young Win 13-6 2453.38 Mar 24th NW Challenge 2018
25 Victoria Win 10-8 1994.4 Mar 24th NW Challenge 2018
5 Washington Loss 6-13 1451.41 Mar 24th NW Challenge 2018
24 Western Washington Win 13-11 1970.91 Mar 24th NW Challenge 2018
3 Oregon Loss 12-15 1888.28 Mar 25th NW Challenge 2018
5 Washington Loss 10-15 1597.8 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)