#35 Cal Poly-SLO (8-8)

avg: 1505.64  •  sd: 72.38  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
32 Brigham Young Loss 6-9 1131.32 Jan 24th Santa Barbara Invite 2020
37 Utah Loss 12-13 1355.83 Jan 25th Santa Barbara Invite 2020
18 Wisconsin Loss 7-13 1283.69 Jan 25th Santa Barbara Invite 2020
26 California-Davis Loss 6-13 1064.28 Jan 25th Santa Barbara Invite 2020
48 Victoria Win 12-11 1489.48 Jan 26th Santa Barbara Invite 2020
51 California-Santa Cruz Win 10-5 1921.44 Jan 26th Santa Barbara Invite 2020
60 Massachusetts Loss 7-8 1148.88 Feb 15th Presidents Day Invite 2020
71 Oregon Win 8-7 1307.77 Feb 15th Presidents Day Invite 2020
81 Southern California Win 10-7 1425.21 Feb 15th Presidents Day Invite 2020
8 California-Santa Barbara Loss 5-12 1435.49 Feb 15th Presidents Day Invite 2020
60 Massachusetts Win 10-5 1847.78 Feb 16th Presidents Day Invite 2020
41 Whitman College Win 11-7 1891.61 Feb 16th Presidents Day Invite 2020
40 Colorado College Win 11-10 1583.35 Feb 16th Presidents Day Invite 2020
19 UCLA Loss 7-9 1558.67 Feb 17th Presidents Day Invite 2020
8 California-Santa Barbara Loss 6-12 1456.18 Feb 17th Presidents Day Invite 2020
39 Chicago Win 14-4 2059.18 Feb 17th Presidents Day Invite 2020
**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)