#241 defunCT (0-17)

avg: -161.42  •  sd: 138.49  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
94 Log Jam** Loss 3-14 477.67 Ignored Jul 6th AntlerLock 2019
104 Burly** Loss 4-15 425.93 Ignored Jul 6th AntlerLock 2019
163 One Night Loss 9-14 237.26 Jul 6th AntlerLock 2019
225 Highlight Reel Loss 9-10 109.55 Jul 6th AntlerLock 2019
17 Sprout** Loss 0-15 1167.5 Ignored Jul 7th AntlerLock 2019
225 Highlight Reel Loss 7-12 -285.96 Jul 7th AntlerLock 2019
51 Lantern** Loss 1-13 758.82 Ignored Aug 24th The Incident 2019 Age of Ultimatron
121 Genny The Boys** Loss 2-13 329.67 Ignored Aug 24th The Incident 2019 Age of Ultimatron
175 Bomb Squad** Loss 0-13 32.86 Ignored Aug 24th The Incident 2019 Age of Ultimatron
157 Ender's Outcasts** Loss 1-13 142.66 Ignored Aug 24th The Incident 2019 Age of Ultimatron
232 Bees Loss 3-9 -496.07 Aug 25th The Incident 2019 Age of Ultimatron
206 Sky Hook Loss 4-10 -172.88 Aug 25th The Incident 2019 Age of Ultimatron
- Festive Salmon Loss 3-13 -209.65 Sep 7th Metro New York Mens Club Sectional Championship 2019
87 Magma Bears** Loss 2-13 505.39 Ignored Sep 7th Metro New York Mens Club Sectional Championship 2019
53 Colt** Loss 1-13 740.96 Ignored Sep 7th Metro New York Mens Club Sectional Championship 2019
237 Stuyvesant Sticky Fingers Alumni Loss 12-13 -175.48 Sep 8th Metro New York Mens Club Sectional Championship 2019
204 Spring Break '93 Loss 4-15 -167.72 Sep 8th Metro New York 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)