#38 Victoria (5-8)

avg: 2020.2  •  sd: 71.86  •  top 16/20: 0.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
33 UCLA Win 10-9 2187.69 Jan 27th Santa Barbara Invitational 2018
17 California-Santa Barbara Loss 8-13 1824.1 Jan 27th Santa Barbara Invitational 2018
26 California Loss 8-13 1637.07 Jan 27th Santa Barbara Invitational 2018
61 California-Davis Win 13-8 2314.08 Jan 27th Santa Barbara Invitational 2018
17 California-Santa Barbara Loss 10-11 2195.26 Jan 28th Santa Barbara Invitational 2018
4 Stanford** Loss 5-13 2095.52 Ignored Jan 28th Santa Barbara Invitational 2018
44 Colorado State Win 10-9 2106.6 Jan 28th Santa Barbara Invitational 2018
2 California-San Diego Loss 7-15 2132.82 Mar 24th NW Challenge 2018
17 California-Santa Barbara Loss 9-14 1846.39 Mar 24th NW Challenge 2018
35 Cal Poly-SLO Win 11-8 2402.16 Mar 24th NW Challenge 2018
36 Colorado College Loss 8-11 1667.57 Mar 24th NW Challenge 2018
19 Vermont Loss 3-15 1663.48 Mar 25th NW Challenge 2018
43 Southern California Win 13-10 2318.42 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)