#4 BFG (18-6)

avg: 1959.6  •  sd: 45.04  •  top 16/20: 99.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
77 Bullet Train** Win 15-6 1762.69 Ignored Jul 8th TCT Pro Elite Challenge West 2023
51 Classy Win 13-9 1802.37 Jul 8th TCT Pro Elite Challenge West 2023
15 Mischief Win 13-12 1955.88 Jul 8th TCT Pro Elite Challenge West 2023
25 MOONDOG Win 13-11 1877.12 Jul 9th TCT Pro Elite Challenge West 2023
17 Lawless Win 13-12 1888.19 Jul 9th TCT Pro Elite Challenge West 2023
15 Mischief Win 15-7 2430.88 Jul 9th TCT Pro Elite Challenge West 2023
2 Drag'n Thrust Win 14-13 2197.18 Aug 4th 2023 US Open Club Championships ICC
8 NOISE Loss 13-14 1768.89 Aug 4th 2023 US Open Club Championships ICC
7 XIST Win 15-14 2045.86 Aug 5th 2023 US Open Club Championships ICC
8 NOISE Win 15-5 2493.89 Aug 5th 2023 US Open Club Championships ICC
3 AMP Loss 13-15 1853.67 Aug 6th 2023 US Open Club Championships ICC
18 Polar Bears Win 15-12 2061.7 Sep 2nd TCT Pro Championships 2023
21 Love Tractor Loss 10-11 1582.03 Sep 2nd TCT Pro Championships 2023
7 XIST Win 14-11 2234.2 Sep 2nd TCT Pro Championships 2023
2 Drag'n Thrust Loss 13-14 1947.18 Sep 3rd TCT Pro Championships 2023
3 AMP Win 15-14 2192.85 Sep 3rd TCT Pro Championships 2023
16 Hybrid Win 15-13 2038.03 Sep 3rd TCT Pro Championships 2023
2 Drag'n Thrust Loss 9-15 1556.7 Sep 4th TCT Pro Championships 2023
11 Seattle Mixtape Win 14-13 1999.03 Sep 23rd 2023 Northwest Mixed Regional Championship
23 Oregon Scorch Win 15-13 1890.94 Sep 23rd 2023 Northwest Mixed Regional Championship
63 Pegasus** Win 15-5 1859.42 Ignored Sep 23rd 2023 Northwest Mixed Regional Championship
77 Bullet Train Win 15-8 1727.5 Sep 23rd 2023 Northwest Mixed Regional Championship
11 Seattle Mixtape Loss 13-14 1749.03 Sep 24th 2023 Northwest Mixed Regional Championship
25 MOONDOG Win 14-10 2046.98 Sep 24th 2023 Northwest 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)