#231 Buffalo Brain Freeze (7-12)

avg: 511.11  •  sd: 70.01  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
73 Petey's Pirates** Loss 2-11 674.68 Ignored Jul 6th Motown Throwdown 2019
187 Pixel Loss 5-10 167.06 Jul 6th Motown Throwdown 2019
257 Derby City Thunder Loss 4-6 11.42 Jul 6th Motown Throwdown 2019
282 Sabers Win 13-4 776.95 Jul 7th Motown Throwdown 2019
263 SlipStream Win 10-6 831.71 Jul 7th Motown Throwdown 2019
289 Taco Cat Win 10-6 463.25 Jul 7th Motown Throwdown 2019
236 Skyhawks Loss 8-10 221.27 Jul 7th Motown Throwdown 2019
173 Alt Stacks Loss 7-13 234.63 Aug 3rd Philly Open 2019
216 Espionage Win 11-8 984.56 Aug 3rd Philly Open 2019
117 PS Loss 3-10 499.32 Aug 3rd Philly Open 2019
194 Nautilus Loss 3-13 103.4 Aug 3rd Philly Open 2019
237 Stormborn Win 13-10 809.78 Aug 4th Philly Open 2019
191 LORD Loss 6-11 167.21 Aug 4th Philly Open 2019
264 Albany Airbenders Win 11-4 931.85 Sep 7th Upstate New York Mixed Club Sectional Championship 2019
178 Mashed Loss 6-10 283.08 Sep 7th Upstate New York Mixed Club Sectional Championship 2019
91 Garbage Plates Loss 6-10 699.96 Sep 7th Upstate New York Mixed Club Sectional Championship 2019
201 Zen Loss 7-9 413.56 Sep 7th Upstate New York Mixed Club Sectional Championship 2019
185 Townies Win 10-9 873.1 Sep 8th Upstate New York Mixed Club Sectional Championship 2019
151 Buffalo Lake Effect Loss 3-10 323.09 Sep 8th Upstate 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)