#211 Arizona (6-6)

avg: 772.8  •  sd: 81.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
93 Colorado-B Loss 5-13 666.1 Jan 27th New Year Fest 40
178 Brigham Young-B Win 9-7 1192.23 Jan 27th New Year Fest 40
109 Denver Loss 7-12 676.33 Jan 27th New Year Fest 40
135 Kansas Loss 10-13 779.1 Jan 27th New Year Fest 40
310 Arizona State-B Win 11-10 427.77 Jan 28th New Year Fest 40
327 Denver-B Win 9-3 789.75 Jan 28th New Year Fest 40
202 California-B Win 9-8 951.52 Mar 30th 2024 Sinvite
234 Claremont Win 10-3 1303.14 Mar 30th 2024 Sinvite
292 California-Santa Barbara-B Win 12-7 923.16 Mar 30th 2024 Sinvite
110 Arizona State Loss 4-7 696.43 Mar 30th 2024 Sinvite
227 Cal State-Long Beach Loss 7-9 442.66 Mar 31st 2024 Sinvite
202 California-B Loss 4-7 330.36 Mar 31st 2024 Sinvite
**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)