#164 Espionage (12-13)

avg: 771.56  •  sd: 62.91  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
104 Legion Loss 4-11 434.18 Jun 24th Seven Cities Show Down
195 Swampbenders Win 11-5 1148.56 Jun 24th Seven Cities Show Down
91 Brackish Loss 0-11 486.1 Jun 24th Seven Cities Show Down
201 Spice Loss 4-8 -40.88 Jun 24th Seven Cities Show Down
106 Ant Madness Loss 4-11 427.9 Jun 24th Seven Cities Show Down
195 Swampbenders Win 14-11 861.89 Jun 25th Seven Cities Show Down
234 Voltage Win 14-3 841.9 Jun 25th Seven Cities Show Down
141 PS Win 12-9 1189.93 Aug 5th Philly Open 2023
234 Voltage Win 10-5 815.8 Aug 5th Philly Open 2023
216 Brooklyn Hive Win 10-7 794.74 Aug 5th Philly Open 2023
24 Loco** Loss 3-13 1070.85 Ignored Aug 6th Philly Open 2023
95 Scarecrow Win 9-7 1347.84 Aug 6th Philly Open 2023
55 Garbage Plates Loss 4-13 742.27 Aug 6th Philly Open 2023
55 Garbage Plates Loss 9-12 996.9 Aug 19th Philly Invite 2023
50 Jughandle Loss 9-14 919.58 Aug 19th Philly Invite 2023
146 Heavy Flow Win 11-9 1073.2 Aug 19th Philly Invite 2023
141 PS Loss 8-13 348.41 Aug 20th Philly Invite 2023
213 Milk Win 11-10 552.35 Aug 20th Philly Invite 2023
155 NY Swipes Loss 9-11 541.21 Aug 20th Philly Invite 2023
46 Revival** Loss 5-14 836.06 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
81 Fireball Loss 6-12 561.94 Sep 9th 2023 Mixed Capital Sectional Championship
214 Deep Cut Win 10-4 1026.05 Sep 10th 2023 Mixed Capital Sectional Championship
214 Deep Cut Win 12-5 1026.05 Sep 10th 2023 Mixed Capital Sectional Championship
234 Voltage Win 14-6 841.9 Sep 10th 2023 Mixed Capital Sectional Championship
106 Ant Madness Loss 6-15 427.9 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)