#40 Pittsburgh Port Authority (14-6)

avg: 1528.9  •  sd: 53.4  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
12 Rally Loss 10-15 1404.29 Jun 24th FROGS
70 League of Shadows Win 13-12 1372 Jun 24th FROGS
6 XIST Loss 3-15 1414.52 Jun 24th FROGS
8 Sprocket Loss 7-15 1350.89 Jun 24th FROGS
48 Wild Card Win 14-12 1662.78 Jun 25th FROGS
45 Darkwing Win 13-12 1583.13 Jul 15th Boston Invite 2023
65 Grand Army Win 11-10 1399.14 Jul 15th Boston Invite 2023
61 Funk Loss 8-9 1184.97 Jul 15th Boston Invite 2023
6 XIST Loss 9-10 1889.52 Jul 15th Boston Invite 2023
68 Heat Wave Win 14-12 1479 Aug 19th Philly Invite 2023
213 Milk** Win 15-6 1041.23 Ignored Aug 19th Philly Invite 2023
105 Legion Win 15-4 1643.98 Aug 19th Philly Invite 2023
67 HVAC Win 15-10 1715.95 Aug 20th Philly Invite 2023
49 Jughandle Win 14-11 1720.11 Aug 20th Philly Invite 2023
68 Heat Wave Win 14-9 1731.91 Aug 20th Philly Invite 2023
178 Philly Twist Win 13-7 1229.59 Sep 9th 2023 Mixed Founders Sectional Championship
213 Milk** Win 13-5 1041.23 Ignored Sep 9th 2023 Mixed Founders Sectional Championship
98 Farm Show Win 13-7 1631.37 Sep 9th 2023 Mixed Founders Sectional Championship
49 Jughandle Win 11-9 1655.98 Sep 10th 2023 Mixed Founders Sectional Championship
26 Loco Loss 7-10 1306.86 Sep 10th 2023 Mixed 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)