#14 Rally (16-5)

avg: 1833.74  •  sd: 99.06  •  top 16/20: 68.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
6 Sprocket Win 15-11 2306.13 Jun 24th FROGS
65 League of Shadows Win 15-9 1764.17 Jun 24th FROGS
38 Pittsburgh Port Authority Win 15-10 1954.71 Jun 24th FROGS
7 XIST Loss 8-15 1356.05 Jun 24th FROGS
12 'Shine Win 15-11 2232.25 Jun 25th FROGS
22 Storm Win 15-10 2142.95 Jul 15th TCT Pro Elite Challenge East 2023
5 Cleveland Crocs Loss 7-14 1353.62 Jul 15th TCT Pro Elite Challenge East 2023
9 Space Force Win 13-11 2119.54 Jul 15th TCT Pro Elite Challenge East 2023
24 Loco Loss 9-14 1196.98 Jul 16th TCT Pro Elite Challenge East 2023
42 The Chad Larson Experience Win 12-9 1826.4 Aug 19th TCT Elite Select Challenge 2023
20 Toro Win 14-12 1958.44 Aug 19th TCT Elite Select Challenge 2023
16 Hybrid Loss 10-11 1698.85 Aug 19th TCT Elite Select Challenge 2023
19 Public Enemy Win 11-10 1863.53 Aug 20th TCT Elite Select Challenge 2023
1 shame. Loss 8-15 1602.4 Aug 20th TCT Elite Select Challenge 2023
5 Cleveland Crocs Win 12-11 2061.51 Aug 20th TCT Elite Select Challenge 2023
252 Pumphouse** Win 13-2 353.94 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
146 Heavy Flow** Win 13-3 1423.99 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
177 District Cocktails** Win 13-3 1249.62 Ignored Sep 9th 2023 Mixed Capital Sectional Championship
104 Legion** Win 13-5 1634.18 Ignored Sep 10th 2023 Mixed Capital Sectional Championship
66 HVAC Win 15-4 1844.45 Sep 10th 2023 Mixed Capital Sectional Championship
59 Greater Baltimore Anthem Win 15-4 1909.03 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)