#6 Cal Poly-SLO (21-6)

avg: 2127.62  •  sd: 47.92  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
8 Brigham Young Loss 11-13 1841.72 Jan 24th Santa Barbara Invite 2025
33 California-Santa Barbara Win 13-9 2079.49 Jan 25th Santa Barbara Invite 2025
29 Utah Valley Win 13-9 2170.82 Jan 25th Santa Barbara Invite 2025
26 Utah Win 13-4 2395.58 Jan 26th Santa Barbara Invite 2025
7 Washington Win 11-9 2365.34 Jan 26th Santa Barbara Invite 2025
14 California Win 7-4 2465.39 Jan 26th Santa Barbara Invite 2025
173 California-Davis Win 13-6 1545.35 Feb 15th Presidents Day Invite 2025
7 Washington Win 12-10 2354.25 Feb 15th Presidents Day Invite 2025
41 California-San Diego Win 13-10 1949.61 Feb 16th Presidents Day Invite 2025
42 Stanford Win 13-7 2174.9 Feb 16th Presidents Day Invite 2025
10 Oregon State Win 13-9 2400.24 Feb 16th Presidents Day Invite 2025
2 Colorado Loss 12-13 2107.88 Feb 16th Presidents Day Invite 2025
9 California-Santa Cruz Win 13-11 2250.02 Feb 17th Presidents Day Invite 2025
7 Washington Win 13-11 2344.97 Feb 17th Presidents Day Invite 2025
118 British Columbia -B** Win 13-5 1786.01 Ignored Mar 8th Stanford Invite 2025 Mens
53 Whitman Win 13-11 1777.13 Mar 8th Stanford Invite 2025 Mens
41 California-San Diego Win 13-9 2040.03 Mar 8th Stanford Invite 2025 Mens
12 British Columbia Win 12-11 2096.97 Mar 9th Stanford Invite 2025 Mens
14 California Win 13-8 2465.39 Mar 9th Stanford Invite 2025 Mens
9 California-Santa Cruz Loss 10-13 1693.04 Mar 9th Stanford Invite 2025 Mens
28 Pittsburgh Win 13-9 2183.3 Mar 29th Easterns 2025
21 Georgia Tech Win 13-8 2350.95 Mar 29th Easterns 2025
37 North Carolina-Wilmington Win 13-7 2192.6 Mar 29th Easterns 2025
4 Carleton College Loss 12-13 2078.31 Mar 29th Easterns 2025
2 Colorado Loss 10-15 1779.28 Mar 30th Easterns 2025
14 California Win 15-11 2350.39 Mar 30th Easterns 2025
4 Carleton College Loss 13-14 2078.31 Mar 30th Easterns 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)