#57 Utah (6-4)

avg: 1406.45  •  sd: 120.99  •  top 16/20: 0.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
16 British Columbia Loss 6-11 1230.32 Jan 28th Santa Barbara Invitational 2023
43 Grand Canyon Win 13-12 1636.91 Jan 28th Santa Barbara Invitational 2023
8 Cal Poly-SLO Loss 6-15 1332.95 Jan 28th Santa Barbara Invitational 2023
82 California-Santa Barbara Win 9-5 1804.32 Jan 28th Santa Barbara Invitational 2023
47 Case Western Reserve Win 13-6 2055.38 Jan 29th Santa Barbara Invitational 2023
7 Oregon Loss 8-15 1403.27 Jan 29th Santa Barbara Invitational 2023
30 Utah State Loss 3-5 1232.13 Jan 29th Santa Barbara Invitational 2023
316 Idaho** Win 15-2 678.82 Ignored Mar 4th Big Sky Brawl1
- Montana State Win 9-7 1055.83 Mar 4th Big Sky Brawl1
184 Nevada-Reno Win 10-8 1071.62 Mar 4th Big Sky Brawl1
**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)