#23 Cal Poly-SLO (13-11)

avg: 1971.29  •  sd: 87.75  •  top 16/20: 10.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
11 Brigham Young Loss 12-13 2193.86 Jan 26th Santa Barbara Invite 2024
10 Washington Loss 9-13 1930.21 Jan 27th Santa Barbara Invite 2024
3 Carleton College** Loss 4-15 2106.55 Ignored Jan 27th Santa Barbara Invite 2024
24 California-Davis Loss 7-10 1581.43 Jan 27th Santa Barbara Invite 2024
82 Northwestern** Win 13-5 1932.46 Ignored Jan 28th Santa Barbara Invite 2024
15 California-San Diego Win 11-7 2629.44 Jan 28th Santa Barbara Invite 2024
55 Southern California Win 12-8 1997.24 Feb 3rd Stanford Open 2024
- Cal Poly-Humboldt** Win 13-0 561.39 Ignored Feb 3rd Stanford Open 2024
45 Portland Win 9-8 1809.91 Feb 3rd Stanford Open 2024
138 California-B** Win 13-1 1496.13 Ignored Feb 3rd Stanford Open 2024
55 Southern California Win 11-6 2102.78 Feb 17th Presidents Day Invite 2024
9 California-Santa Barbara Loss 8-14 1885.77 Feb 17th Presidents Day Invite 2024
19 Colorado State Loss 5-14 1513.2 Feb 17th Presidents Day Invite 2024
124 Claremont Win 12-6 1549.13 Feb 18th Presidents Day Invite 2024
30 California Loss 7-9 1605.22 Feb 18th Presidents Day Invite 2024
113 Denver** Win 15-4 1664.82 Ignored Feb 18th Presidents Day Invite 2024
24 California-Davis Loss 2-9 1371.09 Feb 19th Presidents Day Invite 2024
30 California Win 9-7 2163.89 Feb 19th Presidents Day Invite 2024
6 Stanford Loss 4-10 1958.63 Mar 2nd Stanford Invite 2024
10 Washington Win 8-7 2473.78 Mar 2nd Stanford Invite 2024
18 Victoria Win 9-7 2400.7 Mar 2nd Stanford Invite 2024
9 California-Santa Barbara Loss 5-8 1968.2 Mar 2nd Stanford Invite 2024
10 Washington Loss 9-10 2223.78 Mar 3rd Stanford Invite 2024
46 Texas Win 8-6 1977.49 Mar 3rd Stanford 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)