#84 Pittsburgh Stealers (14-5)

avg: 1327.67  •  sd: 66.37  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
80 Rumspringa Loss 12-13 1240.61 Aug 5th Philly Open 2023
226 Buffalo Frostbite** Win 13-4 1086.54 Ignored Aug 5th Philly Open 2023
228 Mischief** Win 13-1 1066.63 Ignored Aug 5th Philly Open 2023
80 Rumspringa Loss 12-13 1240.61 Aug 6th Philly Open 2023
133 BAG Loss 8-10 771.64 Aug 6th Philly Open 2023
52 Oakgrove Boys Win 13-12 1652.68 Aug 6th Philly Open 2023
166 Enigma Win 13-6 1439.67 Aug 19th Motown Throwdown 2023
88 Black Lung Loss 9-12 957.38 Aug 19th Motown Throwdown 2023
139 Hazard Win 12-7 1494.64 Aug 19th Motown Throwdown 2023
130 Diesel Win 10-4 1640.66 Aug 20th Motown Throwdown 2023
63 I-69 Win 10-7 1838.98 Aug 20th Motown Throwdown 2023
117 Chimney Win 11-8 1485.18 Aug 20th Motown Throwdown 2023
180 SUPA FC Win 13-9 1207.95 Sep 9th 2023 Mens Founders Sectional Championship
177 JAWN Win 13-6 1412.44 Sep 9th 2023 Mens Founders Sectional Championship
164 Pride of Rowan Newark Win 13-9 1277.29 Sep 9th 2023 Mens Founders Sectional Championship
244 Wooder** Win 13-2 820.17 Ignored Sep 9th 2023 Mens Founders Sectional Championship
180 SUPA FC Win 15-9 1304.87 Sep 16th 2023 Mens Founders Sectional Championship
91 Helots Loss 11-15 913.72 Sep 16th 2023 Mens Founders Sectional Championship
162 Delco Club Win 15-5 1463.83 Sep 16th 2023 Mens Founders Sectional Championship
**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)