#7 Cal Poly-SLO (24-6)

avg: 2175.35  •  sd: 44.73  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
2 Brigham Young Loss 7-13 1760.77 Jan 27th Santa Barbara Invitational 2023
16 British Columbia Win 11-10 2117.55 Jan 28th Santa Barbara Invitational 2023
53 Utah Win 15-6 2219.99 Jan 28th Santa Barbara Invitational 2023
73 California-Santa Barbara Win 15-7 2091.64 Jan 28th Santa Barbara Invitational 2023
42 Grand Canyon Win 14-4 2305.28 Jan 28th Santa Barbara Invitational 2023
17 Washington Win 12-9 2335.51 Jan 29th Santa Barbara Invitational 2023
10 California-Santa Cruz Win 11-7 2556.63 Jan 29th Santa Barbara Invitational 2023
18 California Win 13-8 2457.73 Jan 29th Santa Barbara Invitational 2023
47 Colorado State Win 12-9 1992.59 Feb 18th President’s Day Invite
17 Washington Win 14-12 2211.1 Feb 18th President’s Day Invite
73 California-Santa Barbara Win 10-5 2065.54 Feb 18th President’s Day Invite
32 Oregon State Win 12-8 2246.88 Feb 19th President’s Day Invite
29 Utah State Win 10-9 1963.27 Feb 19th President’s Day Invite
10 California-Santa Cruz Win 12-11 2214.74 Feb 19th President’s Day Invite
57 Stanford Win 14-3 2182.25 Feb 19th President’s Day Invite
9 Oregon Loss 9-12 1791.77 Feb 20th President’s Day Invite
17 Washington Win 13-10 2318.28 Feb 20th President’s Day Invite
32 Oregon State Win 13-8 2301.89 Mar 4th Stanford Invite Mens
73 California-Santa Barbara** Win 13-2 2091.64 Ignored Mar 4th Stanford Invite Mens
18 California Win 12-8 2402.72 Mar 4th Stanford Invite Mens
58 California-San Diego Win 11-10 1706.41 Mar 5th Stanford Invite Mens
17 Washington Win 11-10 2115.14 Mar 5th Stanford Invite Mens
10 California-Santa Cruz Loss 6-7 1964.74 Mar 5th Stanford Invite Mens
19 Georgia Win 11-8 2316.46 Apr 1st Easterns 2023
13 Tufts Win 13-8 2564.38 Apr 1st Easterns 2023
5 Vermont Win 12-8 2651.22 Apr 1st Easterns 2023
23 Wisconsin Loss 9-11 1645.31 Apr 1st Easterns 2023
9 Oregon Win 14-12 2358.1 Apr 2nd Easterns 2023
8 Pittsburgh Loss 12-13 2030.18 Apr 2nd Easterns 2023
1 North Carolina Loss 12-15 2092.16 Apr 2nd Easterns 2023
**Blowout Eligible

FAQ

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
  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
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)