#34 UCLA (13-9)

avg: 1728.73  •  sd: 58.53  •  top 16/20: 0.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
90 Santa Clara Win 13-11 1615.7 Jan 26th Santa Barbara Invite 2019
6 Brigham Young Loss 9-13 1716.17 Jan 26th Santa Barbara Invite 2019
42 British Columbia Loss 12-13 1548.61 Jan 26th Santa Barbara Invite 2019
19 Colorado State Loss 6-13 1299.55 Jan 26th Santa Barbara Invite 2019
19 Colorado State Loss 10-12 1661.43 Jan 27th Santa Barbara Invite 2019
10 Washington Loss 8-13 1548.35 Jan 27th Santa Barbara Invite 2019
51 Western Washington Win 13-5 2229.76 Jan 27th Santa Barbara Invite 2019
180 Humboldt State** Win 13-4 1658.43 Ignored Feb 9th Stanford Open 2019
133 Utah State Win 11-8 1610.88 Feb 9th Stanford Open 2019
168 Whitworth Win 13-7 1644.76 Feb 9th Stanford Open 2019
90 Santa Clara Loss 6-7 1261.86 Feb 10th Stanford Open 2019
3 Oregon Loss 9-13 1770.42 Feb 16th Presidents Day Invite 2019
51 Western Washington Win 10-8 1892.43 Feb 16th Presidents Day Invite 2019
5 Cal Poly-SLO Loss 4-10 1544.46 Feb 17th Presidents Day Invite 2019
100 California-Santa Cruz Win 12-1 1958.77 Feb 17th Presidents Day Invite 2019
42 British Columbia Win 12-5 2273.61 Feb 18th Presidents Day Invite 2019
8 Colorado Win 9-8 2220.44 Feb 18th Presidents Day Invite 2019
74 Arizona Win 12-11 1604.09 Mar 23rd Trouble in Vegas 2019
125 Colorado School of Mines Win 13-4 1878.32 Mar 23rd Trouble in Vegas 2019
76 Utah Win 13-10 1801.87 Mar 23rd Trouble in Vegas 2019
65 Florida Loss 10-12 1297.62 Mar 24th Trouble in Vegas 2019
133 Utah State Win 13-6 1845.27 Mar 24th Trouble in Vegas 2019
**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)