#222 I-79 (4-16)

avg: 384.34  •  sd: 59.27  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
122 Huntsville Outlaws Loss 6-11 470.03 Jul 21st Bourbon Bash 2018
- Spidermonkeys Win 11-6 315.75 Jul 21st Bourbon Bash 2018
134 Petey's Pirates Loss 2-11 357.75 Jul 21st Bourbon Bash 2018
246 Taco Cat Win 10-4 503.83 Jul 21st Bourbon Bash 2018
205 Fifth Element Loss 4-11 -56.01 Jul 21st Bourbon Bash 2018
129 Moonshine Loss 6-13 371.57 Jul 22nd Bourbon Bash 2018
- Pocket City Approach Win 11-5 865.94 Jul 22nd Bourbon Bash 2018
198 Second Wind Loss 6-8 280.76 Jul 22nd Bourbon Bash 2018
125 Hybrid** Loss 6-15 407.61 Ignored Aug 11th Michigan Mix Up 2018
208 Bonfire Loss 12-15 234.2 Aug 11th Michigan Mix Up 2018
203 Zen Loss 10-15 97.09 Aug 12th Michigan Mix Up 2018
203 Zen Win 15-13 764.87 Aug 12th Michigan Mix Up 2018
140 Rocket LawnChair Loss 7-15 295.66 Aug 12th Michigan Mix Up 2018
163 Stoke Loss 3-11 196.61 Sep 8th Founders Mixed Sectional Championship 2018
117 The Process Loss 7-9 753.05 Sep 8th Founders Mixed Sectional Championship 2018
152 Peep Show Loss 3-9 255.36 Sep 8th Founders Mixed Sectional Championship 2018
26 Alloy** Loss 2-11 989.35 Ignored Sep 8th Founders Mixed Sectional Championship 2018
18 Loco** Loss 1-11 1134.4 Ignored Sep 8th Founders Mixed Sectional Championship 2018
158 Philly Twist Loss 6-11 268.41 Sep 9th Founders Mixed Sectional Championship 2018
163 Stoke Loss 8-10 533.95 Sep 9th Founders Mixed 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)