#46 Revival (14-8)

avg: 1436.06  •  sd: 67.62  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
177 District Cocktails** Win 13-3 1249.62 Ignored Jun 24th Seven Cities Show Down
124 Magnanimouse Win 13-4 1561.58 Jun 24th Seven Cities Show Down
234 Voltage** Win 13-2 841.9 Ignored Jun 24th Seven Cities Show Down
66 HVAC Win 12-8 1685.61 Jun 24th Seven Cities Show Down
201 Spice** Win 15-5 1123.93 Ignored Jun 25th Seven Cities Show Down
106 Ant Madness Win 15-8 1592.71 Jun 25th Seven Cities Show Down
91 Brackish Win 15-12 1386.59 Jun 25th Seven Cities Show Down
42 The Chad Larson Experience Loss 10-11 1356.04 Jul 15th TCT Pro Elite Challenge East 2023
16 Hybrid Loss 8-12 1382.7 Jul 15th TCT Pro Elite Challenge East 2023
12 'Shine Loss 6-13 1251.08 Jul 15th TCT Pro Elite Challenge East 2023
30 Waterloo Loss 10-12 1377.19 Jul 16th TCT Pro Elite Challenge East 2023
52 Roma Ultima Win 10-8 1616.76 Jul 29th TCT Select Flight East 2023
25 MOONDOG Loss 8-11 1282.67 Jul 29th TCT Select Flight East 2023
28 Flight Club Loss 3-13 1035.27 Jul 29th TCT Select Flight East 2023
73 Northern Comfort Win 15-6 1794.2 Jul 30th TCT Select Flight East 2023
47 Darkwing Win 12-6 1998.81 Jul 30th TCT Select Flight East 2023
27 Chicago Parlay Loss 8-12 1196.36 Jul 30th TCT Select Flight East 2023
164 Espionage** Win 14-5 1371.56 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
214 Deep Cut** Win 15-4 1026.05 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
81 Fireball Win 10-5 1715.15 Sep 10th 2023 Mixed Capital Sectional Championship
59 Greater Baltimore Anthem Loss 6-15 709.03 Sep 10th 2023 Mixed Capital Sectional Championship
81 Fireball Win 10-5 1715.15 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)