#54 Cal Poly-SLO (10-13)

avg: 1252.19  •  sd: 72.31  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
6 Brigham Young** Loss 6-15 1554.83 Ignored Jan 27th Santa Barbara Invitational 2023
12 California-Santa Barbara Loss 6-12 1353.47 Jan 28th Santa Barbara Invitational 2023
29 UCLA Win 10-9 1661.89 Jan 28th Santa Barbara Invitational 2023
71 Utah Loss 2-14 496.07 Jan 28th Santa Barbara Invitational 2023
74 Lewis & Clark Loss 7-8 924.24 Jan 29th Santa Barbara Invitational 2023
43 Wisconsin Loss 6-9 955.81 Jan 29th Santa Barbara Invitational 2023
68 Northwestern Win 7-6 1240.06 Jan 29th Santa Barbara Invitational 2023
23 Carleton College-Eclipse Loss 4-6 1239.5 Feb 4th Stanford Open
176 Chico State** Win 12-1 823.79 Ignored Feb 4th Stanford Open
75 Nevada-Reno Win 7-4 1536.19 Feb 4th Stanford Open
23 Carleton College-Eclipse Loss 5-10 1031.21 Feb 5th Stanford Open
- Humboldt State** Win 13-2 789.46 Ignored Feb 5th Stanford Open
110 California-San Diego-B Win 8-2 1411.42 Feb 5th Stanford Open
33 Portland Win 10-8 1764.63 Feb 5th Stanford Open
3 Colorado** Loss 2-15 1740.96 Ignored Feb 18th President’s Day Invite
24 California-Davis Loss 4-12 993.24 Feb 18th President’s Day Invite
30 California Win 9-7 1811.25 Feb 18th President’s Day Invite
71 Utah Win 8-5 1549.68 Feb 18th President’s Day Invite
85 Southern California Win 8-6 1257.48 Feb 19th President’s Day Invite
8 Stanford** Loss 3-10 1507.22 Ignored Feb 19th President’s Day Invite
25 Duke Loss 5-9 1039.68 Feb 19th President’s Day Invite
71 Utah Loss 5-6 971.07 Feb 19th President’s Day Invite
30 California Loss 7-9 1252.57 Feb 20th President’s Day Invite
**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)