#175 Philly Twist (6-13)

avg: 655.55  •  sd: 50.95  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
177 District Cocktails Loss 7-11 182.72 Aug 5th Philly Open 2023
178 Eat Lightning Win 8-7 774.39 Aug 5th Philly Open 2023
111 Lampshade Loss 9-11 756.21 Aug 5th Philly Open 2023
119 Mashed Loss 9-10 858.99 Aug 5th Philly Open 2023
149 ColorBomb Loss 7-10 415.65 Aug 6th Philly Open 2023
142 Goosebumps Loss 6-10 347.45 Aug 6th Philly Open 2023
78 Deadweight Loss 8-11 796.03 Aug 19th Philly Invite 2023
106 Ant Madness Loss 8-14 491.86 Aug 19th Philly Invite 2023
66 HVAC Loss 7-10 854.79 Aug 19th Philly Invite 2023
104 Legion Loss 10-15 580.57 Aug 20th Philly Invite 2023
155 NY Swipes Loss 10-12 552.3 Aug 20th Philly Invite 2023
213 Milk Win 11-7 894.25 Aug 20th Philly Invite 2023
38 Pittsburgh Port Authority Loss 7-13 943.58 Sep 9th 2023 Mixed Founders Sectional Championship
213 Milk Win 11-7 894.25 Sep 9th 2023 Mixed Founders Sectional Championship
242 Ultra Instinct Win 13-9 529.47 Sep 9th 2023 Mixed Founders Sectional Championship
141 PS Loss 7-11 377.68 Sep 10th 2023 Mixed Founders Sectional Championship
227 The Incidentals Win 11-5 913.98 Sep 10th 2023 Mixed Founders Sectional Championship
184 Crucible Win 7-6 725.41 Sep 10th 2023 Mixed Founders Sectional Championship
141 PS Loss 8-11 478.96 Sep 10th 2023 Mixed Founders 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)