#8 Cal Poly-SLO (20-3)

avg: 1932.95  •  sd: 42.37  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
3 Brigham Young Loss 7-13 1559.98 Jan 27th Santa Barbara Invitational 2023
16 British Columbia Win 11-10 1902.02 Jan 28th Santa Barbara Invitational 2023
57 Utah Win 15-6 2006.45 Jan 28th Santa Barbara Invitational 2023
82 California-Santa Barbara Win 15-7 1875.26 Jan 28th Santa Barbara Invitational 2023
43 Grand Canyon Win 14-4 2111.91 Jan 28th Santa Barbara Invitational 2023
20 Washington Win 12-9 2087.42 Jan 29th Santa Barbara Invitational 2023
9 California-Santa Cruz Win 11-7 2362.36 Jan 29th Santa Barbara Invitational 2023
26 California Win 13-8 2189.27 Jan 29th Santa Barbara Invitational 2023
52 Colorado State Win 12-9 1781.83 Feb 18th President’s Day Invite
20 Washington Win 14-12 1963.01 Feb 18th President’s Day Invite
82 California-Santa Barbara Win 10-5 1849.16 Feb 18th President’s Day Invite
31 Oregon State Win 12-8 2072.18 Feb 19th President’s Day Invite
30 Utah State Win 10-9 1775.7 Feb 19th President’s Day Invite
9 California-Santa Cruz Win 12-11 2020.46 Feb 19th President’s Day Invite
66 Stanford Win 14-3 1972.5 Feb 19th President’s Day Invite
7 Oregon Loss 9-12 1622.71 Feb 20th President’s Day Invite
20 Washington Win 13-10 2070.19 Feb 20th President’s Day Invite
31 Oregon State Win 13-8 2127.18 Mar 4th Stanford Invite Mens
82 California-Santa Barbara** Win 13-2 1875.26 Ignored Mar 4th Stanford Invite Mens
26 California Win 12-8 2134.27 Mar 4th Stanford Invite Mens
64 California-San Diego Win 11-10 1504.59 Mar 5th Stanford Invite Mens
20 Washington Win 11-10 1867.05 Mar 5th Stanford Invite Mens
9 California-Santa Cruz Loss 6-7 1770.46 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)