#88 Mango (16-11)

avg: 1125.86  •  sd: 56.83  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
41 BW Ultimate Loss 8-15 935.46 Jun 10th Bay Area Ultimate Classic 2023
35 LIT Ultimate Loss 9-15 1047.6 Jun 10th Bay Area Ultimate Classic 2023
179 VU Win 15-7 1271.77 Jun 10th Bay Area Ultimate Classic 2023
47 Donuts Loss 7-11 976.49 Jun 11th Bay Area Ultimate Classic 2023
198 Air Throwmads Win 15-7 1156.25 Jun 11th Bay Area Ultimate Classic 2023
134 Firefly Win 15-5 1497.39 Jun 11th Bay Area Ultimate Classic 2023
113 Shipwreck Win 10-4 1621.03 Jul 8th Revolution 2023
172 Nebula Win 11-6 1282.96 Jul 8th Revolution 2023
59 Grit City Win 9-8 1437.32 Jul 8th Revolution 2023
91 Hive Win 9-5 1632.46 Jul 8th Revolution 2023
69 Robot Win 11-8 1619.89 Jul 9th Revolution 2023
62 American Barbecue Loss 8-10 1027.04 Jul 9th Revolution 2023
41 BW Ultimate Loss 12-14 1279.32 Jul 9th Revolution 2023
113 Shipwreck Win 8-7 1146.03 Aug 12th Flower Power 2023
198 Air Throwmads Win 13-4 1156.25 Aug 12th Flower Power 2023
151 Spoiler Alert Win 9-8 949.59 Aug 12th Flower Power 2023
80 Flagstaff Ultimate Loss 4-13 577.04 Aug 12th Flower Power 2023
113 Shipwreck Win 13-8 1517.19 Aug 13th Flower Power 2023
102 Party Wave Win 12-8 1494.2 Aug 13th Flower Power 2023
134 Firefly Loss 9-12 552.02 Aug 13th Flower Power 2023
211 Quails Win 13-9 878.66 Sep 9th 2023 Mixed Nor Cal Sectional Championship
47 Donuts Loss 7-13 885.85 Sep 9th 2023 Mixed Nor Cal Sectional Championship
21 Sunshine Loss 7-11 1266.8 Sep 9th 2023 Mixed Nor Cal Sectional Championship
134 Firefly Win 10-9 1022.39 Sep 9th 2023 Mixed Nor Cal Sectional Championship
44 Classy Loss 5-15 859.93 Sep 10th 2023 Mixed Nor Cal Sectional Championship
60 Cutthroat Loss 10-14 911.58 Sep 10th 2023 Mixed Nor Cal Sectional Championship
179 VU Win 13-7 1229.31 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)