#136 Pipeline (5-14)

avg: 580.82  •  sd: 56.77  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
169 Bearfest Win 11-4 634.92 Jul 28th 2018 Richmond Stonewalled
27 Turbine** Loss 3-11 792.19 Ignored Jul 28th 2018 Richmond Stonewalled
114 Cockfight Loss 6-11 216.78 Jul 28th 2018 Richmond Stonewalled
135 Helots Win 10-9 711.33 Jul 28th 2018 Richmond Stonewalled
125 Town Hall Stars Win 13-12 798.51 Jul 29th 2018 Richmond Stonewalled
- Foggy Bottom Boys Loss 9-11 641.93 Jul 29th 2018 Richmond Stonewalled
123 Satellite Loss 10-13 373.01 Aug 18th Cooler Classic 30
116 Greater Gary Goblins X Loss 9-13 322.59 Aug 18th Cooler Classic 30
60 DeMo Loss 9-13 678.01 Aug 18th Cooler Classic 30
47 MKE Loss 10-13 896.23 Aug 18th Cooler Classic 30
95 Scythe Loss 8-11 513.46 Aug 19th Cooler Classic 30
73 Greater Gary Goblins Y Loss 13-15 801.62 Aug 19th Cooler Classic 30
163 Hippie Mafia Win 15-7 748.52 Aug 19th Cooler Classic 30
36 CLE Smokestack Loss 7-13 742.1 Sep 8th East Plains Mens Sectional Championship 2018
146 Dirty D Loss 10-12 175.11 Sep 8th East Plains Mens Sectional Championship 2018
65 Mango Tree Loss 7-13 486.64 Sep 8th East Plains Mens Sectional Championship 2018
149 Chimney Win 11-6 893.72 Sep 9th East Plains Mens Sectional Championship 2018
112 Enigma Loss 9-13 345.87 Sep 9th East Plains Mens Sectional Championship 2018
104 Black Lung Loss 8-10 546.05 Sep 9th East Plains Mens Sectional Championship 2018
**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)