#32 UCLA (10-14)

avg: 1851.87  •  sd: 60.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
9 California-Santa Barbara Loss 3-11 1821.8 Jan 27th Santa Barbara Invite 2024
82 Northwestern Win 15-7 1932.46 Jan 27th Santa Barbara Invite 2024
5 Oregon** Loss 3-15 2005.7 Ignored Jan 27th Santa Barbara Invite 2024
18 Victoria Loss 9-13 1702.79 Jan 27th Santa Barbara Invite 2024
9 California-Santa Barbara Loss 5-9 1892.74 Jan 28th Santa Barbara Invite 2024
27 Utah Win 10-5 2495.83 Jan 28th Santa Barbara Invite 2024
6 Stanford** Loss 3-13 1958.63 Ignored Feb 17th Presidents Day Invite 2024
124 Claremont** Win 13-3 1569.82 Ignored Feb 17th Presidents Day Invite 2024
27 Utah Loss 8-9 1796.93 Feb 17th Presidents Day Invite 2024
55 Southern California Win 11-8 1921.7 Feb 18th Presidents Day Invite 2024
24 California-Davis Loss 7-9 1691.75 Feb 18th Presidents Day Invite 2024
69 California-San Diego-B Win 9-5 1951.43 Feb 18th Presidents Day Invite 2024
30 California Win 10-6 2380.71 Feb 19th Presidents Day Invite 2024
24 California-Davis Win 9-6 2389.66 Feb 19th Presidents Day Invite 2024
1 British Columbia** Loss 1-13 2294.15 Ignored Mar 2nd Stanford Invite 2024
15 California-San Diego Loss 4-12 1562.55 Mar 2nd Stanford Invite 2024
46 Texas Loss 6-8 1376.5 Mar 2nd Stanford Invite 2024
24 California-Davis Loss 3-7 1371.09 Mar 3rd Stanford Invite 2024
18 Victoria Loss 7-10 1731.69 Mar 3rd Stanford Invite 2024
7 Tufts** Loss 2-15 1920.18 Ignored Mar 30th East Coast Invite 2024
17 Pennsylvania Loss 7-13 1567.17 Mar 30th East Coast Invite 2024
114 West Chester** Win 8-1 1660.72 Ignored Mar 30th East Coast Invite 2024
52 Yale Win 12-7 2114.56 Mar 30th East Coast Invite 2024
41 SUNY-Binghamton Win 8-7 1828.59 Mar 31st East Coast Invite 2024
**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)