#28 Lights Out (15-9)

avg: 1643.61  •  sd: 60.6  •  top 16/20: 0.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
82 Pegasus Win 11-6 1792.29 Jun 15th Northwest Mixer 2019
43 Birdfruit Loss 7-11 1050.9 Jun 15th Northwest Mixer 2019
3 Seattle Mixtape Loss 4-11 1483.72 Jun 15th Northwest Mixer 2019
87 Garbage Win 11-6 1752.06 Jun 15th Northwest Mixer 2019
11 Lochsa Loss 7-8 1758.75 Jun 15th Northwest Mixer 2019
41 BW Ultimate Win 12-11 1664.26 Jul 13th TCT Select Flight Invite West 2019
24 MOONDOG Loss 9-13 1291.87 Jul 13th TCT Select Flight Invite West 2019
64 The Administrators Win 15-8 1909.76 Jul 13th TCT Select Flight Invite West 2019
26 Public Enemy Loss 8-13 1202.13 Jul 14th TCT Select Flight Invite West 2019
51 Minnesota Star Power Win 13-7 2016.56 Jul 14th TCT Select Flight Invite West 2019
56 Grand Army Win 11-8 1767.47 Jul 14th TCT Select Flight Invite West 2019
6 BFG Loss 5-13 1367.11 Aug 17th Northwest Fruit Bowl 2019
48 Classy Loss 10-11 1341.35 Aug 17th Northwest Fruit Bowl 2019
29 Lotus Win 12-9 1988.45 Aug 17th Northwest Fruit Bowl 2019
60 Rubix Win 11-9 1614.11 Aug 17th Northwest Fruit Bowl 2019
51 Minnesota Star Power Win 13-7 2016.56 Aug 18th Northwest Fruit Bowl 2019
3 Seattle Mixtape Loss 10-13 1755.57 Aug 18th Northwest Fruit Bowl 2019
60 Rubix Win 13-8 1861.06 Aug 18th Northwest Fruit Bowl 2019
29 Lotus Loss 8-12 1201.93 Aug 18th Northwest Fruit Bowl 2019
144 T.T Win 13-6 1547.43 Sep 7th Washington Mixed Club Sectional Championship 2019
250 Friendzone** Win 13-3 1016.85 Ignored Sep 7th Washington Mixed Club Sectional Championship 2019
152 Fable** Win 13-5 1522.02 Ignored Sep 7th Washington Mixed Club Sectional Championship 2019
87 Garbage Win 13-7 1762.9 Sep 7th Washington Mixed Club Sectional Championship 2019
82 Pegasus Win 14-7 1828.48 Sep 8th Washington Mixed Club Sectional Championship 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)