#349 Grand Canyon-B (1-8)

avg: 62.64  •  sd: 169.08  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
117 Colorado Mines** Loss 1-15 704.9 Ignored Jan 28th New Year Fest 2023
184 Denver** Loss 4-17 407.98 Ignored Jan 28th New Year Fest 2023
152 Northern Arizona** Loss 6-15 543.35 Ignored Jan 28th New Year Fest 2023
55 Utah Valley** Loss 3-13 1014.18 Ignored Jan 29th New Year Fest 2023
356 Denver-B Win 8-7 62.71 Jan 29th New Year Fest 2023
179 Loyola Marymount** Loss 2-15 432.73 Ignored Apr 1st Southwest Showdown
152 Northern Arizona** Loss 6-15 543.35 Ignored Apr 1st Southwest Showdown
149 Cal Poly-SLO-B** Loss 5-13 555.09 Ignored Apr 2nd Southwest Showdown
221 California-B** Loss 2-15 254.61 Ignored Apr 2nd Southwest Showdown
**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)