#135 Helots (4-12)

avg: 586.33  •  sd: 92.52  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
27 Turbine** Loss 3-11 792.19 Ignored Jul 28th 2018 Richmond Stonewalled
114 Cockfight Loss 1-11 163.48 Jul 28th 2018 Richmond Stonewalled
52 Oakgrove Boys** Loss 4-11 586.93 Ignored Jul 28th 2018 Richmond Stonewalled
136 Pipeline Loss 9-10 455.82 Jul 28th 2018 Richmond Stonewalled
148 Bomb Squad Loss 7-10 -19.23 Jul 29th 2018 Richmond Stonewalled
118 Adelphos Loss 10-13 390.24 Aug 11th Nuccis Cup 2018
- Bearproof Win 13-6 985.45 Aug 11th Nuccis Cup 2018
87 Westchester Magma Bears Loss 6-13 313.88 Aug 11th Nuccis Cup 2018
109 JAWN Loss 12-13 644.86 Aug 11th Nuccis Cup 2018
157 Winc City Fog of War Win 13-10 621.36 Aug 12th Nuccis Cup 2018
77 Deathsquad Loss 9-13 562.36 Aug 12th Nuccis Cup 2018
58 Rumspringa Loss 10-13 770.38 Sep 8th Founders Mens Sectional Championship 2018
18 Pittsburgh Temper** Loss 2-13 1071.4 Ignored Sep 8th Founders Mens Sectional Championship 2018
- Trenton Takers Win 13-7 889.73 Sep 8th Founders Mens Sectional Championship 2018
- Lehigh Win 13-4 883.87 Sep 8th Founders Mens Sectional Championship 2018
118 Adelphos Loss 13-14 593.38 Sep 15th Founders 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)