#178 Eat Lightning (8-13)

avg: 649.39  •  sd: 45.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
221 Replay Win 8-7 483.97 Jul 15th Boston Invite 2023
78 Deadweight Loss 1-13 561.64 Jul 15th Boston Invite 2023
132 Harfang ultimate Loss 5-13 283.89 Jul 15th Boston Invite 2023
154 Moontower Loss 6-11 247.9 Jul 15th Boston Invite 2023
84 Buffalo Lake Effect Loss 7-11 663.32 Aug 5th Philly Open 2023
146 Heavy Flow Loss 8-11 458.38 Aug 5th Philly Open 2023
242 Ultra Instinct Win 11-5 710.9 Aug 5th Philly Open 2023
175 Philly Twist Loss 7-8 530.55 Aug 5th Philly Open 2023
216 Brooklyn Hive Win 8-5 858.68 Aug 6th Philly Open 2023
195 Swampbenders Win 10-7 938.22 Aug 6th Philly Open 2023
149 ColorBomb Win 10-9 930.31 Aug 26th The Incident 2023
111 Lampshade Loss 4-13 405.42 Aug 26th The Incident 2023
216 Brooklyn Hive Win 11-8 770.68 Aug 26th The Incident 2023
62 Funk Loss 7-13 704.36 Aug 26th The Incident 2023
147 FLI Win 10-9 943.29 Aug 27th The Incident 2023
62 Funk Loss 5-10 687.99 Aug 27th The Incident 2023
78 Deadweight Loss 5-13 561.64 Sep 9th 2023 Mixed Metro New York Sectional Championship
155 NY Swipes Loss 9-10 665.42 Sep 9th 2023 Mixed Metro New York Sectional Championship
162 Room Temperature Loss 9-10 649.87 Sep 9th 2023 Mixed Metro New York Sectional Championship
71 Grand Army Loss 4-13 604.31 Sep 9th 2023 Mixed Metro New York Sectional Championship
216 Brooklyn Hive Win 12-9 750.44 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)