#56 Ghost Train (15-10)

avg: 1320.48  •  sd: 34.44  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
162 Rip City** Win 13-3 1314.22 Ignored Jun 22nd Eugene Summer Solstice 2019
15 Rhino Slam! Loss 9-13 1399.02 Jun 22nd Eugene Summer Solstice 2019
39 Blackfish Loss 10-11 1305.73 Jun 22nd Eugene Summer Solstice 2019
158 Cojones Win 13-4 1342.54 Jun 22nd Eugene Summer Solstice 2019
74 Dark Star Win 15-14 1307.62 Jun 23rd Eugene Summer Solstice 2019
68 Sawtooth Win 13-11 1465.26 Jun 23rd Eugene Summer Solstice 2019
52 El Niño Loss 13-14 1231.84 Jul 13th TCT Select Flight Invite West 2019
80 ISO Atmo Win 12-11 1271.91 Jul 13th TCT Select Flight Invite West 2019
174 Daybreak Win 15-9 1150.97 Jul 13th TCT Select Flight Invite West 2019
20 CLE Smokestack Loss 10-13 1334.65 Jul 14th TCT Select Flight Invite West 2019
43 MKE Loss 10-11 1286.33 Jul 14th TCT Select Flight Invite West 2019
52 El Niño Loss 11-12 1231.84 Jul 14th TCT Select Flight Invite West 2019
100 Seattle Blacklist Win 15-8 1601.46 Aug 3rd CBR 2019
158 Cojones Win 15-10 1196.14 Aug 3rd CBR 2019
109 Green River Swordfish Win 15-6 1612.08 Aug 3rd CBR 2019
162 Rip City** Win 15-6 1314.22 Ignored Aug 4th CBR 2019
74 Dark Star Win 13-12 1307.62 Aug 4th CBR 2019
39 Blackfish Loss 10-15 977.12 Aug 4th CBR 2019
13 Furious George Loss 7-11 1369.67 Sep 7th Washington Mens Club Sectional Championship 2019
75 DNA Win 11-9 1430.46 Sep 7th Washington Mens Club Sectional Championship 2019
- Waste Management Squad** Win 11-2 1236.72 Ignored Sep 7th Washington Mens Club Sectional Championship 2019
77 SOUF Win 11-8 1521.52 Sep 7th Washington Mens Club Sectional Championship 2019
19 Voodoo Loss 9-15 1175.7 Sep 8th Washington Mens Club Sectional Championship 2019
77 SOUF Loss 13-15 941.73 Sep 8th Washington Mens Club Sectional Championship 2019
75 DNA Win 15-11 1562.42 Sep 8th Washington Mens 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)