#67 Arizona (8-1)

avg: 1433.92  •  sd: 109.94  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
73 San Diego State Loss 7-11 921.47 Jan 27th New Year Fest 40
177 Arizona-B** Win 12-4 1076.56 Ignored Jan 27th New Year Fest 40
116 Arizona State Win 10-2 1633.73 Jan 27th New Year Fest 40
113 Denver Win 9-8 1189.82 Jan 27th New Year Fest 40
73 San Diego State Win 10-7 1778.03 Jan 28th New Year Fest 40
65 Grand Canyon Win 9-8 1573.37 Jan 28th New Year Fest 40
83 Northern Arizona Win 8-7 1455.1 Jan 28th New Year Fest 40
199 Colorado College-B** Win 13-0 844.9 Ignored Mar 2nd Snow Melt 2024
126 Colorado-B Win 11-6 1496.46 Mar 3rd Snow Melt 2024
**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)