#350 Arizona State-B (2-8)

avg: 511.42  •  sd: 116.23  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
381 Denver-B Loss 7-10 -101.89 Jan 27th New Year Fest 40
101 Colorado Mines** Loss 2-13 912.08 Ignored Jan 27th New Year Fest 40
247 Northern Arizona Loss 6-13 365.2 Jan 27th New Year Fest 40
58 Utah Valley** Loss 2-13 1137.54 Ignored Jan 27th New Year Fest 40
219 Arizona Loss 10-11 927.48 Jan 28th New Year Fest 40
381 Denver-B Win 9-4 887.78 Jan 28th New Year Fest 40
235 Claremont Loss 7-9 715.87 Mar 24th Southwest Showdown 2024
211 San Diego State Loss 6-13 480.14 Mar 24th Southwest Showdown 2024
255 Cal State-Long Beach Loss 4-9 322.79 Mar 24th Southwest Showdown 2024
416 San Diego State-B Win 14-6 600 Ignored Mar 24th Southwest Showdown 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)