#174 Alt Stacks (8-9)

avg: 741.84  •  sd: 57.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
180 Varsity Win 10-7 1109 Jul 13th Ow My Knee
242 Pandatime Win 13-3 1012.02 Jul 13th Ow My Knee
201 Nautilus Loss 7-10 243.1 Aug 3rd Philly Open 2019
109 Birds Loss 4-13 478.47 Aug 3rd Philly Open 2019
232 Buffalo Brain Freeze Win 13-7 1010.61 Aug 3rd Philly Open 2019
215 Espionage Win 12-8 1019.48 Aug 3rd Philly Open 2019
123 PS Loss 7-13 474.83 Aug 3rd Philly Open 2019
103 Soft Boiled Loss 5-8 657.14 Aug 4th Philly Open 2019
144 Philly Twist Loss 9-15 376.93 Aug 4th Philly Open 2019
209 TBD Win 15-13 815.79 Aug 4th Philly Open 2019
44 Grand Army** Loss 5-13 861.45 Ignored Sep 7th Metro New York Mixed Club Sectional Championship 2019
268 Skyscrapers Win 15-5 845.32 Sep 7th Metro New York Mixed Club Sectional Championship 2019
180 Varsity Win 11-9 968.55 Sep 7th Metro New York Mixed Club Sectional Championship 2019
57 Metro North Loss 5-15 729.01 Sep 7th Metro New York Mixed Club Sectional Championship 2019
109 Birds Loss 6-14 478.47 Sep 8th Metro New York Mixed Club Sectional Championship 2019
179 Unlimited Swipes Win 13-12 848.99 Sep 8th Metro New York Mixed Club Sectional Championship 2019
124 Funk Loss 12-14 803.62 Sep 8th Metro New York Mixed 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)