#6 BFG (12-4)

avg: 1967.11  •  sd: 56.3  •  top 16/20: 99.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
4 Slow White Win 12-10 2253.52 Jul 13th TCT Pro Elite Challenge 2019
32 NOISE Win 13-10 1939.94 Jul 13th TCT Pro Elite Challenge 2019
8 shame. Loss 9-13 1508.03 Jul 13th TCT Pro Elite Challenge 2019
14 Love Tractor Loss 10-13 1498.16 Jul 14th TCT Pro Elite Challenge 2019
30 No Touching! Win 10-9 1765.73 Jul 14th TCT Pro Elite Challenge 2019
37 Jughandle Win 13-6 2162.97 Jul 14th TCT Pro Elite Challenge 2019
10 Space Heater Win 15-12 2198.13 Aug 2nd 2019 US Open Club Championship
3 Seattle Mixtape Loss 10-15 1630.11 Aug 3rd 2019 US Open Club Championship
2 AMP Loss 11-15 1726.2 Aug 3rd 2019 US Open Club Championship
28 Lights Out Win 13-5 2243.61 Aug 17th Northwest Fruit Bowl 2019
60 Rubix Win 13-8 1861.06 Aug 17th Northwest Fruit Bowl 2019
48 Classy Win 13-9 1884.91 Aug 17th Northwest Fruit Bowl 2019
26 Public Enemy Win 13-6 2298.29 Aug 17th Northwest Fruit Bowl 2019
29 Lotus Win 13-10 1971.23 Aug 18th Northwest Fruit Bowl 2019
26 Public Enemy Win 13-10 2026.43 Aug 18th Northwest Fruit Bowl 2019
3 Seattle Mixtape Win 13-11 2312.56 Aug 18th Northwest Fruit Bowl 2019
**Blowout Eligible

FAQ

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
  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
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)