#14 Cal Poly-SLO (17-9)

avg: 2443.09  •  sd: 80.2  •  top 16/20: 98.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
18 Brigham Young Win 10-7 2682.98 Jan 24th Santa Barbara Invite 2025
4 Colorado Loss 8-11 2384.02 Jan 25th Santa Barbara Invite 2025
23 Pennsylvania Win 7-6 2370.85 Jan 25th Santa Barbara Invite 2025
8 Washington Win 11-10 2652.69 Jan 25th Santa Barbara Invite 2025
2 Carleton College Loss 8-12 2557.41 Jan 26th Santa Barbara Invite 2025
11 Utah Win 7-5 2806.94 Jan 26th Santa Barbara Invite 2025
10 California-San Diego Loss 8-9 2370.97 Jan 26th Santa Barbara Invite 2025
115 Arizona State** Win 11-2 1796.04 Ignored Feb 1st Presidents Day Qualifiers 2025
63 California-Irvine** Win 13-2 2242.75 Ignored Feb 1st Presidents Day Qualifiers 2025
220 California-San Diego-C** Win 13-0 984.47 Ignored Feb 1st Presidents Day Qualifiers 2025
39 California Win 11-1 2467.09 Feb 1st Presidents Day Qualifiers 2025
63 California-Irvine** Win 10-4 2242.75 Ignored Feb 2nd Presidents Day Qualifiers 2025
25 UCLA Win 8-5 2587.05 Feb 2nd Presidents Day Qualifiers 2025
58 Brown** Win 13-4 2267.49 Ignored Mar 1st Stanford Invite 2025 Womens
30 Wisconsin Win 12-9 2368.34 Mar 1st Stanford Invite 2025 Womens
13 Stanford Loss 7-12 1954.57 Mar 1st Stanford Invite 2025 Womens
16 California-Davis Win 8-7 2431.69 Mar 1st Stanford Invite 2025 Womens
10 California-San Diego Win 11-9 2745.18 Mar 2nd Stanford Invite 2025 Womens
3 Tufts Loss 7-9 2560.73 Mar 2nd Stanford Invite 2025 Womens
17 California-Santa Barbara Loss 9-12 1958.49 Mar 2nd Stanford Invite 2025 Womens
8 Washington Win 10-8 2790.36 Mar 22nd Northwest Challenge 2025
2 Carleton College Loss 8-13 2502.41 Mar 22nd Northwest Challenge 2025
43 Colorado State** Win 11-3 2435.82 Ignored Mar 22nd Northwest Challenge 2025
7 Michigan Loss 7-10 2178.95 Mar 23rd Northwest Challenge 2025
13 Stanford Win 10-6 2971.24 Mar 23rd Northwest Challenge 2025
15 Victoria Loss 7-12 1850.64 Mar 23rd Northwest Challenge 2025
**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)