#62 American Barbecue (12-8)

avg: 1289.71  •  sd: 61.11  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
36 Tower Loss 7-8 1427.94 Jul 8th Revolution 2023
60 Cutthroat Win 8-6 1610.77 Jul 8th Revolution 2023
41 BW Ultimate Loss 5-8 1046.67 Jul 8th Revolution 2023
134 Firefly Win 8-5 1350.99 Jul 8th Revolution 2023
47 Donuts Win 10-9 1568.38 Jul 9th Revolution 2023
35 LIT Ultimate Loss 5-14 963.08 Jul 9th Revolution 2023
88 Mango Win 10-8 1388.52 Jul 9th Revolution 2023
90 Stump Win 12-11 1240.58 Aug 12th Kleinman Eruption 2023
224 Night Cap** Win 10-4 938.87 Ignored Aug 12th Kleinman Eruption 2023
101 Igneous Ultimate Win 11-8 1423.98 Aug 12th Kleinman Eruption 2023
77 Seattle Soft Serve Loss 7-9 909.48 Aug 13th Kleinman Eruption 2023
91 Hive Loss 7-9 824.06 Aug 13th Kleinman Eruption 2023
172 Nebula Win 7-3 1336.26 Aug 13th Kleinman Eruption 2023
15 Mischief Loss 5-13 1222.59 Sep 9th 2023 Mixed Nor Cal Sectional Championship
60 Cutthroat Loss 9-11 1061.08 Sep 9th 2023 Mixed Nor Cal Sectional Championship
221 Moonlight Ultimate** Win 13-2 966.77 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
179 VU** Win 14-5 1271.77 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
44 Classy Win 10-9 1584.93 Sep 10th 2023 Mixed Nor Cal Sectional Championship
35 LIT Ultimate Loss 7-11 1096.18 Sep 10th 2023 Mixed Nor Cal Sectional Championship
60 Cutthroat Win 11-6 1856.98 Sep 10th 2023 Mixed Nor Cal 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)