#167 Wake Forest (4-7)

avg: 574.52  •  sd: 138.72  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
53 American** Loss 0-13 984.8 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
171 Michigan-B Loss 3-4 435.18 Feb 17th Commonwealth Cup Weekend 1 2024
144 Catholic Loss 4-10 248.98 Feb 18th Commonwealth Cup Weekend 1 2024
191 Elon Win 8-7 472.39 Feb 18th Commonwealth Cup Weekend 1 2024
181 Virginia-B Win 9-2 1061.64 Feb 18th Commonwealth Cup Weekend 1 2024
28 St Olaf** Loss 0-13 1321.34 Ignored Mar 23rd Needle in a Ho Stack 2024
149 Emory Win 10-1 1403.75 Mar 23rd Needle in a Ho Stack 2024
162 South Carolina-B Loss 3-7 40.77 Mar 24th Needle in a Ho Stack 2024
149 Emory Loss 2-10 203.75 Mar 24th Needle in a Ho Stack 2024
217 Emory-B** Win 11-3 568.5 Mar 24th Needle in a Ho Stack 2024
59 Georgetown** Loss 1-13 919.65 Ignored Mar 24th Needle in a Ho Stack 2024
**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)