#26 Utah State (8-10)

avg: 1769.41  •  sd: 57.44  •  top 16/20: 10.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
5 Cal Poly-SLO Loss 10-11 2049.71 Feb 17th Presidents Day Invite 2024
134 California-Irvine Win 15-9 1624.57 Feb 17th Presidents Day Invite 2024
22 Washington Loss 10-13 1490.85 Feb 17th Presidents Day Invite 2024
35 California-Santa Cruz Win 13-12 1762.21 Feb 18th Presidents Day Invite 2024
65 Stanford Win 11-8 1770.54 Feb 18th Presidents Day Invite 2024
23 UCLA Loss 6-11 1261.75 Feb 18th Presidents Day Invite 2024
24 British Columbia Win 11-9 2049.73 Feb 19th Presidents Day Invite 2024
23 UCLA Loss 8-11 1442.84 Feb 19th Presidents Day Invite 2024
9 Brown Loss 14-15 1900.07 Mar 2nd Smoky Mountain Invite 2024
10 Carleton College Loss 12-13 1885.34 Mar 2nd Smoky Mountain Invite 2024
1 North Carolina Loss 9-13 1870 Mar 2nd Smoky Mountain Invite 2024
8 Vermont Loss 9-13 1620.03 Mar 2nd Smoky Mountain Invite 2024
11 Minnesota Loss 11-15 1621.08 Mar 3rd Smoky Mountain Invite 2024
92 Tennessee Win 15-7 1867.11 Mar 3rd Smoky Mountain Invite 2024
23 UCLA Loss 13-15 1594.27 Mar 3rd Smoky Mountain Invite 2024
30 Utah Win 13-5 2276.99 Mar 29th Utah Valley Rally
178 Brigham Young-B** Win 13-0 1512.89 Ignored Mar 30th Utah Valley Rally
45 Utah Valley Win 13-11 1761.74 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)