#134 Firefly (12-14)

avg: 897.39  •  sd: 44.73  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
47 Donuts Loss 6-13 843.38 Jun 10th Bay Area Ultimate Classic 2023
153 DR Win 12-10 1058.63 Jun 10th Bay Area Ultimate Classic 2023
39 Lotus** Loss 1-15 928.95 Ignored Jun 10th Bay Area Ultimate Classic 2023
41 BW Ultimate** Loss 5-15 900.27 Ignored Jun 11th Bay Area Ultimate Classic 2023
221 Moonlight Ultimate Win 11-7 833.66 Jun 11th Bay Area Ultimate Classic 2023
88 Mango Loss 5-15 525.86 Jun 11th Bay Area Ultimate Classic 2023
69 Robot Loss 4-11 654.28 Jul 8th Revolution 2023
62 American Barbecue Loss 5-8 836.1 Jul 8th Revolution 2023
151 Spoiler Alert Win 7-4 1320.75 Jul 8th Revolution 2023
113 Shipwreck Loss 6-13 421.03 Jul 9th Revolution 2023
198 Air Throwmads Win 11-10 681.25 Jul 9th Revolution 2023
179 VU Win 10-9 796.77 Jul 9th Revolution 2023
244 Erosion** Win 13-2 674.79 Ignored Aug 12th Flower Power 2023
230 Birds of Paradise Win 13-5 905.44 Aug 12th Flower Power 2023
221 Moonlight Ultimate Win 12-5 966.77 Aug 12th Flower Power 2023
35 LIT Ultimate** Loss 4-13 963.08 Ignored Aug 12th Flower Power 2023
69 Robot Loss 5-15 654.28 Aug 13th Flower Power 2023
88 Mango Win 12-9 1471.22 Aug 13th Flower Power 2023
80 Flagstaff Ultimate Loss 4-15 577.04 Aug 13th Flower Power 2023
198 Air Throwmads Win 13-5 1156.25 Sep 9th 2023 Mixed Nor Cal Sectional Championship
44 Classy Loss 9-13 1041.36 Sep 9th 2023 Mixed Nor Cal Sectional Championship
35 LIT Ultimate** Loss 4-13 963.08 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
88 Mango Loss 9-10 1000.86 Sep 9th 2023 Mixed Nor Cal Sectional Championship
153 DR Win 10-9 945.51 Sep 10th 2023 Mixed Nor Cal Sectional Championship
44 Classy Loss 8-15 895.12 Sep 10th 2023 Mixed Nor Cal Sectional Championship
179 VU Win 13-11 900.61 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)