#117 Queen City Kings (7-4)

avg: 830.47  •  sd: 96.86  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
128 Beef Depot Win 11-10 861.83 Jun 28th Seven Cities Showdown 2025
176 Beef Depot Win 15-8 733.02 Jun 28th Seven Cities Showdown 2025
188 Jolly Roger** Win 15-1 405.7 Ignored Jun 28th Seven Cities Showdown 2025
128 Beef Depot Win 11-10 861.83 Jun 29th Seven Cities Showdown 2025
188 Jolly Roger** Win 15-2 405.7 Ignored Jun 29th Seven Cities Showdown 2025
49 Lost Boys Loss 6-13 899.22 Jul 12th Filling the Void Mens Womens 2025
157 Tennessee Folklore Loss 12-13 328.11 Jul 12th Filling the Void Mens Womens 2025
80 Town Hall Stars Loss 7-11 665.88 Jul 12th Filling the Void Mens Womens 2025
120 Pittsburgh Stealers Win 13-10 1133.43 Jul 13th Filling the Void Mens Womens 2025
115 UpRoar Win 11-8 1205.86 Jul 13th Filling the Void Mens Womens 2025
106 Hooch Loss 12-13 777.18 Jul 13th Filling the Void Mens Womens 2025
**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)