#18 Brigham Young (12-4)

avg: 2286.51  •  sd: 91.83  •  top 16/20: 68.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 California-Santa Cruz Win 13-8 2083.91 Jan 27th Santa Barbara Invitational 2018
4 Stanford Loss 9-13 2276.95 Jan 27th Santa Barbara Invitational 2018
44 Colorado State Loss 11-12 1856.6 Jan 27th Santa Barbara Invitational 2018
35 Cal Poly-SLO Win 13-10 2364.69 Jan 27th Santa Barbara Invitational 2018
76 Pacific Lutheran Win 11-9 1941.92 Feb 10th Stanford Open 2018
67 Puget Sound Win 7-6 1893.81 Feb 10th Stanford Open 2018
103 Claremont Win 9-5 2020.27 Feb 10th Stanford Open 2018
131 Air Force Academy** Win 13-2 1885.67 Ignored Mar 3rd Air Force Invite 2018
154 Colorado-B** Win 13-1 1752.21 Ignored Mar 3rd Air Force Invite 2018
104 Denver Win 11-7 1948.4 Mar 3rd Air Force Invite 2018
36 Colorado College Win 11-9 2282.38 Mar 3rd Air Force Invite 2018
16 Western Washington Win 15-9 2859.87 Mar 23rd NW Challenge 2018
6 British Columbia Loss 8-10 2297.47 Mar 23rd NW Challenge 2018
12 Carleton College Win 11-8 2787.58 Mar 24th NW Challenge 2018
19 Vermont Loss 9-12 1918.12 Mar 24th NW Challenge 2018
20 Washington Win 15-7 2840.23 Mar 24th 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)