#18 Brigham Young (16-10)

avg: 1853.38  •  sd: 52.77  •  top 16/20: 76%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
65 California-Santa Barbara Win 13-10 1790.52 Jan 26th Santa Barbara Invitational 2018
20 Cal Poly-SLO Loss 11-12 1718.12 Jan 26th Santa Barbara Invitational 2018
146 Nevada-Reno** Win 13-4 1749.3 Ignored Jan 27th Santa Barbara Invitational 2018
79 California-Davis Win 13-8 1910.79 Jan 27th Santa Barbara Invitational 2018
143 California-San Diego** Win 13-4 1760.92 Ignored Jan 27th Santa Barbara Invitational 2018
24 Western Washington Loss 10-13 1413.92 Jan 27th Santa Barbara Invitational 2018
4 Minnesota Loss 7-13 1512.38 Feb 16th Warm Up A Florida Affair 2018
111 Arizona State Win 13-7 1846.74 Feb 16th Warm Up A Florida Affair 2018
93 Cincinnati Win 13-8 1859.58 Feb 16th Warm Up A Florida Affair 2018
13 Wisconsin Win 12-9 2262.49 Feb 16th Warm Up A Florida Affair 2018
30 Auburn Win 13-8 2205.42 Feb 17th Warm Up A Florida Affair 2018
39 Northwestern Win 13-8 2124.86 Feb 17th Warm Up A Florida Affair 2018
168 South Florida Win 13-7 1621.52 Feb 17th Warm Up A Florida Affair 2018
21 Texas A&M Win 13-11 2050.9 Feb 17th Warm Up A Florida Affair 2018
15 Stanford Loss 13-14 1760.63 Mar 2nd Stanford Invite 2018
7 Pittsburgh Loss 9-13 1568.9 Mar 3rd Stanford Invite 2018
1 North Carolina Loss 10-13 2017.19 Mar 3rd Stanford Invite 2018
32 California Win 13-11 1924.64 Mar 3rd Stanford Invite 2018
3 Oregon Loss 8-15 1623.96 Mar 23rd NW Challenge 2018
24 Western Washington Win 14-11 2055.4 Mar 23rd NW Challenge 2018
5 Washington Loss 10-15 1597.8 Mar 23rd NW Challenge 2018
15 Stanford Loss 13-15 1671.45 Mar 23rd NW Challenge 2018
55 Oregon State Win 13-6 2118.18 Mar 24th NW Challenge 2018
38 Southern California Win 13-7 2191.43 Mar 24th NW Challenge 2018
19 Colorado Loss 6-13 1250.95 Mar 24th NW Challenge 2018
17 Colorado State Win 12-8 2310.91 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)