#17 Victoria (3-4)

avg: 1855.03  •  sd: 85.9  •  top 16/20: 80%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
7 Stanford Loss 11-12 2042.85 Jan 27th Santa Barbara Invite 2024
13 California-Santa Cruz Win 15-12 2202.64 Jan 27th Santa Barbara Invite 2024
12 Brigham Young Loss 8-11 1556.53 Jan 27th Santa Barbara Invite 2024
30 UCLA Win 13-9 2026.04 Jan 27th Santa Barbara Invite 2024
3 British Columbia Loss 3-15 1791.97 Jan 28th Santa Barbara Invite 2024
27 California-Davis Win 9-8 1786.5 Jan 28th Santa Barbara Invite 2024
8 Washington Loss 7-13 1574.97 Jan 28th Santa Barbara Invite 2024
**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)