#388 Arizona-B (4-8)

avg: 280.79  •  sd: 53.45  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
382 Air Force-B Win 11-9 555.34 Jan 26th New Year Fest 2019
272 Arizona State-B Loss 7-13 218.94 Jan 26th New Year Fest 2019
202 Northern Arizona** Loss 2-13 373.07 Ignored Jan 26th New Year Fest 2019
238 Denver Loss 7-13 340.26 Jan 26th New Year Fest 2019
403 Texas-El Paso Loss 8-11 -175.35 Jan 26th New Year Fest 2019
400 Arizona State-C Win 12-11 346.92 Jan 27th New Year Fest 2019
434 Southern California-B Win 11-6 336.41 Mar 23rd Trouble in Vegas 2019
261 Cal Poly-SLO-B Loss 9-13 402.57 Mar 23rd Trouble in Vegas 2019
353 California-San Diego-B Loss 8-10 213.02 Mar 23rd Trouble in Vegas 2019
184 California-B** Loss 4-13 432.51 Ignored Mar 23rd Trouble in Vegas 2019
425 Cal State-Fullerton Win 13-9 439.19 Mar 24th Trouble in Vegas 2019
362 Wisconsin-Oshkosh Loss 10-13 88.23 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)