#277 Arizona-B (0-11)

avg: -328.55  •  sd: 229.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
73 Northern Arizona** Loss 2-13 724.82 Ignored Jan 26th New Year Fest 2019
118 Arizona** Loss 1-13 439.72 Ignored Jan 26th New Year Fest 2019
120 Arizona State** Loss 0-13 427.17 Ignored Jan 26th New Year Fest 2019
74 Denver** Loss 0-13 721.88 Ignored Jan 26th New Year Fest 2019
230 New Mexico** Loss 4-13 -279.33 Jan 27th New Year Fest 2019
86 San Diego State** Loss 1-12 642.2 Ignored Jan 27th New Year Fest 2019
136 Occidental** Loss 2-13 338.15 Ignored Mar 23rd Trouble in Vegas 2019
107 Chico State** Loss 3-13 476.39 Ignored Mar 23rd Trouble in Vegas 2019
230 New Mexico** Loss 2-10 -279.33 Mar 23rd Trouble in Vegas 2019
86 San Diego State** Loss 0-9 642.2 Ignored Mar 23rd Trouble in Vegas 2019
243 Colorado-B Loss 3-11 -409.21 Mar 24th Trouble in Vegas 2019
**Blowout Eligible

FAQ

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
  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
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)