#44 Victoria (8-13)

avg: 1696.72  •  sd: 74.27  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
2 Brigham Young Loss 9-14 1844.44 Jan 28th Santa Barbara Invitational 2023
50 Case Western Reserve Loss 11-12 1515.01 Jan 28th Santa Barbara Invitational 2023
15 UCLA Loss 10-11 1903.29 Jan 28th Santa Barbara Invitational 2023
109 Southern California Win 12-8 1765.07 Jan 28th Santa Barbara Invitational 2023
16 British Columbia Win 10-7 2382.21 Jan 29th Santa Barbara Invitational 2023
9 Oregon Loss 6-10 1640.98 Jan 29th Santa Barbara Invitational 2023
29 Utah State Win 12-11 1963.27 Jan 29th Santa Barbara Invitational 2023
18 California Loss 4-11 1361.57 Jan 29th Santa Barbara Invitational 2023
9 Oregon Loss 9-13 1718.57 Mar 4th Stanford Invite Mens
29 Utah State Loss 10-13 1510.13 Mar 4th Stanford Invite Mens
78 Santa Clara Win 13-5 2075.08 Mar 4th Stanford Invite Mens
32 Oregon State Loss 11-12 1680.73 Mar 5th Stanford Invite Mens
18 California Win 12-7 2482.08 Mar 5th Stanford Invite Mens
23 Wisconsin Win 12-10 2132.64 Mar 5th Stanford Invite Mens
17 Washington Loss 6-12 1410.83 Mar 5th Stanford Invite Mens
232 Chico State Win 14-7 1365.24 Apr 1st Northwest Challenge Mens
32 Oregon State Win 11-10 1930.73 Apr 1st Northwest Challenge Mens
17 Washington Loss 7-15 1390.14 Apr 1st Northwest Challenge Mens
32 Oregon State Loss 8-13 1309.57 Apr 2nd Northwest Challenge Mens
29 Utah State Loss 8-11 1472.66 Apr 2nd Northwest Challenge Mens
86 Dartmouth Loss 8-9 1311.97 Apr 2nd Northwest Challenge Mens
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)