#127 Funk (14-13)

avg: 980.88  •  sd: 71.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
189 DTX Win 12-4 1241.04 Jul 7th AntlerLock 2018
180 HAOS Win 9-8 804.94 Jul 7th AntlerLock 2018
154 Default Win 10-8 1109.8 Jul 7th AntlerLock 2018
102 Titan NE Loss 6-11 560.27 Jul 7th AntlerLock 2018
- VIP Club Loss 11-15 826.42 Jul 8th AntlerLock 2018
57 Heartless Loss 7-15 713.66 Jul 8th AntlerLock 2018
141 Powermove Loss 10-13 565.37 Aug 11th Philly Open 2018
158 Philly Twist Win 11-10 940.1 Aug 11th Philly Open 2018
99 Legion Loss 6-11 568.57 Aug 11th Philly Open 2018
165 Unlimited Swipes Win 13-10 1116.4 Aug 11th Philly Open 2018
156 Heavy Flow Win 12-9 1184.93 Aug 12th Philly Open 2018
117 The Process Loss 8-9 907.39 Aug 12th Philly Open 2018
99 Legion Win 11-5 1715.26 Aug 12th Philly Open 2018
108 Alt Stacks Win 13-8 1573.65 Aug 25th The Incident 2018
132 HVAC Win 13-10 1290.67 Aug 25th The Incident 2018
50 Grand Army Loss 8-15 804.87 Aug 25th The Incident 2018
83 Birds Loss 9-11 976.34 Aug 25th The Incident 2018
165 Unlimited Swipes Win 12-10 1026.38 Aug 26th The Incident 2018
83 Birds Loss 7-15 625.54 Aug 26th The Incident 2018
141 Powermove Loss 10-11 768.51 Aug 26th The Incident 2018
206 Varsity Win 13-7 1100.23 Sep 8th Metro New York Mixed Sectional Championship 2018
66 The Feminists Loss 8-13 787.4 Sep 8th Metro New York Mixed Sectional Championship 2018
165 Unlimited Swipes Win 13-9 1206.82 Sep 8th Metro New York Mixed Sectional Championship 2018
139 Nautilus Win 12-10 1154.02 Sep 9th Metro New York Mixed Sectional Championship 2018
83 Birds Loss 6-12 646.23 Sep 9th Metro New York Mixed Sectional Championship 2018
141 Powermove Win 12-7 1414.03 Sep 9th Metro New York Mixed Sectional Championship 2018
83 Birds Loss 7-13 668.01 Sep 9th Metro New York Mixed Sectional Championship 2018
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)