#30 Utah (13-13)

avg: 1676.99  •  sd: 48.17  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
151 Cal Poly-SLO-B** Win 15-3 1633.94 Ignored Jan 27th Santa Barbara Invite 2024
15 California Loss 10-12 1686.1 Jan 27th Santa Barbara Invite 2024
54 California-Santa Barbara Win 12-9 1815.01 Jan 27th Santa Barbara Invite 2024
53 Colorado State Win 14-7 2053.44 Jan 27th Santa Barbara Invite 2024
24 British Columbia Loss 10-15 1346.92 Jan 28th Santa Barbara Invite 2024
47 Oklahoma Christian Win 11-9 1769.38 Jan 28th Santa Barbara Invite 2024
23 UCLA Loss 10-11 1683.44 Jan 28th Santa Barbara Invite 2024
39 Victoria Win 12-10 1823.85 Jan 28th Santa Barbara Invite 2024
43 California-San Diego Win 12-10 1800.39 Feb 17th Presidents Day Invite 2024
3 Colorado Loss 8-14 1707.09 Feb 17th Presidents Day Invite 2024
65 Stanford Win 10-7 1794.6 Feb 17th Presidents Day Invite 2024
5 Cal Poly-SLO Loss 9-14 1700.84 Feb 18th Presidents Day Invite 2024
6 Oregon Loss 9-12 1763.89 Feb 18th Presidents Day Invite 2024
18 Oregon State Loss 10-15 1415.7 Feb 18th Presidents Day Invite 2024
15 California Loss 7-10 1534.55 Feb 19th Presidents Day Invite 2024
39 Victoria Win 11-10 1710.73 Feb 19th Presidents Day Invite 2024
24 British Columbia Loss 11-14 1487.19 Mar 23rd Northwest Challenge Mens 2024
5 Cal Poly-SLO Loss 13-15 1960.53 Mar 23rd Northwest Challenge Mens 2024
15 California Loss 12-15 1623.73 Mar 23rd Northwest Challenge Mens 2024
22 Washington Win 14-13 1944 Mar 24th Northwest Challenge Mens 2024
39 Victoria Win 15-7 2185.73 Mar 24th Northwest Challenge Mens 2024
178 Brigham Young-B Win 13-7 1470.42 Mar 29th Utah Valley Rally
45 Utah Valley Loss 10-13 1204.76 Mar 29th Utah Valley Rally
26 Utah State Loss 5-13 1169.41 Mar 29th Utah Valley Rally
178 Brigham Young-B Win 13-7 1470.42 Mar 30th Utah Valley Rally
45 Utah Valley Win 13-8 2029.06 Mar 30th Utah Valley Rally
**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)