#163 UCLA-B (8-8)

avg: 980.45  •  sd: 67.68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
86 Arizona State-B-B Loss 7-9 1017.73 Feb 1st 2020 Mens Presidents Day Qualifier
200 California-Santa Barbara-B Loss 6-9 415.44 Feb 1st 2020 Mens Presidents Day Qualifier
185 Cal State-Long Beach Loss 6-9 482.19 Feb 1st 2020 Mens Presidents Day Qualifier
40 Santa Clara** Loss 4-13 1011.92 Ignored Feb 1st 2020 Mens Presidents Day Qualifier
200 California-Santa Barbara-B Win 6-5 959.01 Feb 2nd 2020 Mens Presidents Day Qualifier
284 San Jose State Win 11-6 978.59 Feb 2nd 2020 Mens Presidents Day Qualifier
294 Southern California-B Win 8-3 989.77 Feb 2nd 2020 Mens Presidents Day Qualifier
86 Arizona State-B-B Loss 5-8 843.47 Feb 22nd Pomona Sweethearts 2020
189 Cal Poly-Pomona Win 7-6 1004.26 Feb 22nd Pomona Sweethearts 2020
68 Occidental Loss 4-9 785.25 Feb 22nd Pomona Sweethearts 2020
303 Long Beach State University-B** Win 11-3 911.2 Ignored Feb 22nd Pomona Sweethearts 2020
187 Loyola Marymount Win 11-4 1494.41 Feb 22nd Pomona Sweethearts 2020
214 California-San Diego-B Win 11-3 1380.34 Feb 29th 2nd Annual Claremont Ultimate Classic
88 Claremont Loss 7-8 1158.92 Feb 29th 2nd Annual Claremont Ultimate Classic
68 Occidental Loss 3-10 785.25 Feb 29th 2nd Annual Claremont Ultimate Classic
187 Loyola Marymount Win 8-6 1194.91 Feb 29th 2nd Annual Claremont Ultimate Classic
**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)