#232 Baltimore BENCH (3-15)

avg: 289.24  •  sd: 68.72  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
206 Varsity Loss 4-9 -57.3 Aug 11th Philly Open 2018
83 Birds** Loss 4-13 625.54 Ignored Aug 11th Philly Open 2018
224 Stormborn Loss 4-5 232.45 Aug 11th Philly Open 2018
165 Unlimited Swipes Loss 5-11 188.26 Aug 12th Philly Open 2018
224 Stormborn Win 9-5 886.51 Aug 12th Philly Open 2018
165 Unlimited Swipes Loss 5-15 188.26 Aug 25th The Incident 2018
141 Powermove** Loss 5-15 293.51 Ignored Aug 25th The Incident 2018
158 Philly Twist Loss 5-15 215.1 Aug 25th The Incident 2018
231 BLT Stacks Loss 12-14 77.71 Aug 25th The Incident 2018
237 Turnstyle Loss 7-8 80.28 Aug 26th The Incident 2018
231 BLT Stacks Loss 9-11 49.46 Aug 26th The Incident 2018
101 Tyrannis** Loss 1-11 512.39 Ignored Sep 8th Capital Mixed Sectional Championship 2018
156 Heavy Flow Loss 2-11 239.56 Sep 8th Capital Mixed Sectional Championship 2018
95 Ant Madness** Loss 2-11 547.28 Ignored Sep 8th Capital Mixed Sectional Championship 2018
239 Pandatime Win 10-5 741.07 Sep 8th Capital Mixed Sectional Championship 2018
179 LORD Loss 7-10 291.55 Sep 8th Capital Mixed Sectional Championship 2018
192 RnB Loss 7-11 151.88 Sep 9th Capital Mixed Sectional Championship 2018
224 Stormborn Win 7-4 853.61 Sep 9th Capital 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)