#146 Heavy Flow (10-15)

avg: 823.99  •  sd: 53.29  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
207 Buffalo Brain Freeze Win 13-7 1037.15 Jun 24th LVU’s Disc Days of Summer 2023
147 FLI Loss 6-8 517.8 Jun 24th LVU’s Disc Days of Summer 2023
184 Crucible Win 12-9 945.78 Jun 24th LVU’s Disc Days of Summer 2023
55 Garbage Plates Loss 9-10 1217.27 Jun 24th LVU’s Disc Days of Summer 2023
68 Heat Wave Loss 8-10 973.51 Jun 25th LVU’s Disc Days of Summer 2023
119 Mashed Loss 8-12 542.83 Jun 25th LVU’s Disc Days of Summer 2023
178 Eat Lightning Win 11-8 1015 Aug 5th Philly Open 2023
84 Buffalo Lake Effect Loss 7-12 609.71 Aug 5th Philly Open 2023
97 Farm Show Loss 5-13 455.09 Aug 5th Philly Open 2023
242 Ultra Instinct** Win 13-3 710.9 Ignored Aug 5th Philly Open 2023
177 District Cocktails Win 10-6 1145.78 Aug 6th Philly Open 2023
91 Brackish Loss 9-13 667.53 Aug 6th Philly Open 2023
164 Espionage Loss 9-11 522.35 Aug 19th Philly Invite 2023
55 Garbage Plates Loss 9-15 826.79 Aug 19th Philly Invite 2023
50 Jughandle Loss 7-15 793.45 Aug 19th Philly Invite 2023
141 PS Win 15-11 1225.73 Aug 20th Philly Invite 2023
155 NY Swipes Win 12-11 915.42 Aug 20th Philly Invite 2023
104 Legion Loss 12-15 733.69 Aug 20th Philly Invite 2023
14 Rally** Loss 3-13 1233.74 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
177 District Cocktails Win 13-8 1145.78 Sep 9th 2023 Mixed Capital Sectional Championship
104 Legion Loss 9-13 615.61 Sep 9th 2023 Mixed Capital Sectional Championship
252 Pumphouse** Win 8-0 353.94 Ignored Sep 10th 2023 Mixed Capital Sectional Championship
201 Spice Win 15-4 1123.93 Sep 10th 2023 Mixed Capital Sectional Championship
114 One More Year Loss 11-15 617.16 Sep 10th 2023 Mixed Capital Sectional Championship
91 Brackish Loss 7-12 565.59 Sep 10th 2023 Mixed Capital 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)