#222 Brigham Young-B (5-5)

avg: 863.47  •  sd: 97.09  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
310 Grand Canyon Win 13-3 1156.25 Jan 27th New Year Fest 2018
235 Arizona State-B Win 13-7 1360.19 Jan 27th New Year Fest 2018
366 Arizona-B Win 13-7 873.55 Jan 27th New Year Fest 2018
90 Northern Arizona Loss 6-13 777.6 Jan 27th New Year Fest 2018
159 Colorado-B Win 12-10 1332.68 Jan 27th New Year Fest 2018
146 Nevada-Reno Loss 10-12 911.18 Mar 3rd Big Sky Brawl 2018
191 Montana State Loss 5-11 370.44 Mar 3rd Big Sky Brawl 2018
353 Montana-B Win 8-6 658.49 Mar 3rd Big Sky Brawl 2018
57 Whitman** Loss 4-11 906.56 Ignored Mar 3rd Big Sky Brawl 2018
206 Washington State Loss 8-11 557.96 Mar 3rd Big Sky Brawl 2018
**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)