#189 Starfire (6-16)

avg: 608.1  •  sd: 56.83  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
142 PS Loss 3-13 256.51 Jun 24th LVU’s Disc Days of Summer 2023
83 Buffalo Lake Effect Loss 5-9 631.58 Jun 24th LVU’s Disc Days of Summer 2023
120 Mashed Loss 6-13 388.8 Jun 24th LVU’s Disc Days of Summer 2023
57 Greater Baltimore Anthem Loss 6-11 771.45 Jun 24th LVU’s Disc Days of Summer 2023
154 ColorBomb Loss 5-9 289.64 Jun 25th LVU’s Disc Days of Summer 2023
207 Buffalo Brain Freeze Win 10-9 601.39 Jun 25th LVU’s Disc Days of Summer 2023
76 Obscure Loss 5-13 591.64 Jul 15th Boston Invite 2023
166 Lobrid Win 11-9 1012.43 Jul 15th Boston Invite 2023
118 Lampshade Loss 7-12 485.64 Jul 15th Boston Invite 2023
72 Townies Loss 9-13 817.47 Jul 15th Boston Invite 2023
226 The Incidentals Win 11-9 576.51 Aug 26th The Incident 2023
157 NY Swipes Win 11-7 1279.56 Aug 26th The Incident 2023
118 Lampshade Loss 6-14 406.15 Aug 27th The Incident 2023
98 Farm Show Loss 6-13 473.83 Aug 27th The Incident 2023
243 NYWT Win 13-4 714.61 Aug 27th The Incident 2023
98 Farm Show Loss 6-13 473.83 Aug 27th The Incident 2023
149 FLI Loss 5-15 229.82 Aug 27th The Incident 2023
149 FLI Loss 6-12 250.51 Sep 9th 2023 Mixed Metro New York Sectional Championship
68 Heat Wave** Loss 5-13 658.05 Ignored Sep 9th 2023 Mixed Metro New York Sectional Championship
216 Brooklyn Hive Win 10-5 991.9 Sep 9th 2023 Mixed Metro New York Sectional Championship
61 Funk Loss 6-13 709.97 Sep 9th 2023 Mixed Metro New York Sectional Championship
157 NY Swipes Loss 9-10 687.66 Sep 10th 2023 Mixed Metro New York 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)