#6 Brigham Young (24-3)

avg: 2134.73  •  sd: 41.92  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
45 California-Santa Barbara Win 13-10 1991.4 Jan 25th Santa Barbara Invite 2019
5 Cal Poly-SLO Win 13-9 2563.03 Jan 25th Santa Barbara Invite 2019
90 Santa Clara** Win 13-2 1986.86 Ignored Jan 26th Santa Barbara Invite 2019
42 British Columbia Win 13-10 2001.75 Jan 26th Santa Barbara Invite 2019
34 UCLA Win 13-9 2147.29 Jan 26th Santa Barbara Invite 2019
19 Colorado State Win 13-7 2457.08 Jan 26th Santa Barbara Invite 2019
136 South Florida Win 13-7 1794.56 Feb 8th Florida Warm Up 2019
12 Texas Win 13-12 2134.9 Feb 8th Florida Warm Up 2019
13 Wisconsin Loss 14-15 1875.97 Feb 8th Florida Warm Up 2019
27 LSU Win 13-9 2196.3 Feb 8th Florida Warm Up 2019
4 Pittsburgh Win 12-11 2309.92 Feb 9th Florida Warm Up 2019
49 Northwestern Win 13-9 2056.26 Feb 9th Florida Warm Up 2019
68 Cincinnati** Win 13-5 2115.37 Ignored Feb 9th Florida Warm Up 2019
7 Carleton College-CUT Win 13-10 2446.78 Feb 9th Florida Warm Up 2019
50 Stanford Win 13-10 1960.88 Mar 1st Stanford Invite 2019
49 Northwestern Win 11-9 1886.9 Mar 2nd Stanford Invite 2019
14 Ohio State Loss 10-11 1867.06 Mar 2nd Stanford Invite 2019
21 California Loss 10-11 1718.46 Mar 2nd Stanford Invite 2019
3 Oregon Win 15-14 2313.99 Mar 2nd Stanford Invite 2019
50 Stanford Win 13-8 2128.9 Mar 23rd 2019 NW Challenge Mens Tier 1
58 Whitman Win 13-9 1998.22 Mar 23rd 2019 NW Challenge Mens Tier 1
42 British Columbia Win 13-7 2231.14 Mar 23rd 2019 NW Challenge Mens Tier 1
30 Victoria Win 13-8 2262.06 Mar 23rd 2019 NW Challenge Mens Tier 1
59 Oregon State Win 13-8 2058.35 Mar 25th 2019 NW Challenge Mens Tier 1
10 Washington Win 13-12 2169.51 Mar 25th 2019 NW Challenge Mens Tier 1
5 Cal Poly-SLO Win 13-9 2563.03 Mar 25th 2019 NW Challenge Mens Tier 1
51 Western Washington Win 13-6 2229.76 Mar 25th 2019 NW Challenge Mens Tier 1
**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)