#76 Stanford (5-14)

avg: 1306.5  •  sd: 56.08  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
7 Oregon Loss 4-15 1301.45 Jan 28th Santa Barbara Invitational 2023
24 California Loss 7-9 1346.99 Jan 28th Santa Barbara Invitational 2023
71 Northwestern Loss 9-13 903.11 Jan 28th Santa Barbara Invitational 2023
47 Western Washington Win 9-8 1546.36 Jan 28th Santa Barbara Invitational 2023
94 California-Santa Barbara Win 10-5 1783.44 Jan 29th Santa Barbara Invitational 2023
119 Southern California Loss 8-9 942.82 Jan 29th Santa Barbara Invitational 2023
6 Colorado Loss 8-14 1386.39 Feb 18th President’s Day Invite
31 Oregon State Loss 8-10 1305.49 Feb 18th President’s Day Invite
74 California-San Diego Loss 8-10 1050.43 Feb 18th President’s Day Invite
30 Utah State Loss 9-12 1239.37 Feb 19th President’s Day Invite
8 Cal Poly-SLO Loss 3-14 1266.69 Feb 19th President’s Day Invite
9 California-Santa Cruz Loss 9-10 1702.88 Feb 19th President’s Day Invite
94 California-Santa Barbara Win 13-9 1628.1 Feb 20th President’s Day Invite
47 Western Washington Loss 9-13 1002.79 Feb 20th President’s Day Invite
16 British Columbia Loss 6-13 1110.73 Mar 4th Stanford Invite Mens
56 Colorado State Loss 8-10 1105.12 Mar 4th Stanford Invite Mens
19 Washington Loss 10-12 1437.09 Mar 4th Stanford Invite Mens
94 California-Santa Barbara Win 9-8 1334.54 Mar 5th Stanford Invite Mens
91 Santa Clara Win 11-9 1467.58 Mar 5th Stanford Invite 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)