#16 Hybrid (13-8)

avg: 1823.85  •  sd: 62.53  •  top 16/20: 71.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
46 Revival Win 12-8 1877.21 Jul 15th TCT Pro Elite Challenge East 2023
12 'Shine Win 15-10 2304.69 Jul 15th TCT Pro Elite Challenge East 2023
42 The Chad Larson Experience Loss 9-10 1356.04 Jul 15th TCT Pro Elite Challenge East 2023
3 AMP Loss 12-13 1942.85 Jul 16th TCT Pro Elite Challenge East 2023
14 Rally Win 11-10 1958.74 Aug 19th TCT Elite Select Challenge 2023
20 Toro Win 14-13 1862.48 Aug 19th TCT Elite Select Challenge 2023
42 The Chad Larson Experience Win 12-10 1719.16 Aug 19th TCT Elite Select Challenge 2023
19 Public Enemy Win 12-9 2083.89 Aug 20th TCT Elite Select Challenge 2023
6 Sprocket Loss 8-15 1360.15 Aug 20th TCT Elite Select Challenge 2023
5 Cleveland Crocs Loss 11-12 1811.51 Aug 20th TCT Elite Select Challenge 2023
13 Slow Win 14-13 1974.58 Sep 2nd TCT Pro Championships 2023
3 AMP Loss 9-15 1552.37 Sep 2nd TCT Pro Championships 2023
8 NOISE Loss 6-15 1293.89 Sep 2nd TCT Pro Championships 2023
20 Toro Win 14-13 1862.48 Sep 2nd TCT Pro Championships 2023
4 BFG Loss 13-15 1745.42 Sep 3rd TCT Pro Championships 2023
7 XIST Loss 9-12 1575.5 Sep 3rd TCT Pro Championships 2023
18 Polar Bears Win 15-12 2061.7 Sep 4th TCT Pro Championships 2023
29 RAMP Win 15-8 2187.56 Sep 23rd 2023 Great Lakes Mixed Regional Championship
165 Prion** Win 15-2 1361.72 Ignored Sep 23rd 2023 Great Lakes Mixed Regional Championship
107 Columbus Chaos** Win 15-0 1627.53 Ignored Sep 23rd 2023 Great Lakes Mixed Regional Championship
5 Cleveland Crocs Win 15-14 2061.51 Sep 24th 2023 Great Lakes Mixed Regional 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)