#15 Stanford (13-8)

avg: 1885.63  •  sd: 73.39  •  top 16/20: 84.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
38 Southern California Win 13-12 1758.89 Jan 27th Santa Barbara Invitational 2018
53 UCLA Win 13-11 1763.26 Jan 27th Santa Barbara Invitational 2018
74 Washington University Win 13-4 2018.49 Jan 27th Santa Barbara Invitational 2018
25 Victoria Loss 14-15 1606.74 Jan 27th Santa Barbara Invitational 2018
20 Cal Poly-SLO Win 13-10 2171.26 Jan 28th Santa Barbara Invitational 2018
17 Colorado State Loss 8-13 1373.6 Jan 28th Santa Barbara Invitational 2018
25 Victoria Win 12-8 2172.89 Jan 28th Santa Barbara Invitational 2018
18 Brigham Young Win 14-13 1978.38 Mar 2nd Stanford Invite 2018
6 Brown Loss 6-13 1446.71 Mar 3rd Stanford Invite 2018
11 Emory Win 12-8 2361.83 Mar 3rd Stanford Invite 2018
5 Washington Loss 10-12 1813.28 Mar 3rd Stanford Invite 2018
20 Cal Poly-SLO Loss 8-12 1401.97 Mar 4th Stanford Invite 2018
32 California Win 11-9 1945.01 Mar 4th Stanford Invite 2018
19 Colorado Loss 7-13 1293.42 Mar 4th Stanford Invite 2018
18 Brigham Young Win 15-13 2067.56 Mar 23rd NW Challenge 2018
24 Western Washington Win 15-8 2306.87 Mar 23rd NW Challenge 2018
3 Oregon Loss 7-13 1631.24 Mar 24th NW Challenge 2018
55 Oregon State Win 13-7 2075.71 Mar 24th NW Challenge 2018
43 British Columbia Win 12-10 1832.77 Mar 24th NW Challenge 2018
3 Oregon Loss 10-15 1735.17 Mar 25th NW Challenge 2018
5 Washington Win 15-9 2566.89 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)