#235 Buffalo Open (1-15)

avg: 10.32  •  sd: 82.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
93 Mango Tree** Loss 2-13 483.08 Jul 6th Motown Throwdown 2019
184 Chimney Loss 4-13 -43.63 Jul 6th Motown Throwdown 2019
123 CaSTLe Loss 5-11 323.61 Jul 6th Motown Throwdown 2019
228 Flying Dutchmen Win 11-9 470.6 Jul 7th Motown Throwdown 2019
193 Midnight Meat Train Loss 5-13 -94.92 Jul 7th Motown Throwdown 2019
184 Chimney Loss 2-13 -43.63 Jul 7th Motown Throwdown 2019
220 Genny Lite Loss 8-13 -215.76 Aug 3rd Philly Open 2019
200 NEO Loss 10-13 127.76 Aug 3rd Philly Open 2019
112 Somerville BAG** Loss 3-13 393.38 Ignored Aug 3rd Philly Open 2019
37 Tanasi** Loss 3-13 875.25 Ignored Aug 3rd Philly Open 2019
170 Adelphos** Loss 2-13 62.14 Ignored Aug 4th Philly Open 2019
230 Hot Tamales Loss 9-14 -295.87 Aug 4th Philly Open 2019
220 Genny Lite Loss 3-10 -319.6 Sep 7th Upstate New York Mens Club Sectional Championship 2019
118 Shrike** Loss 1-13 352.47 Ignored Sep 7th Upstate New York Mens Club Sectional Championship 2019
139 Overcast** Loss 4-13 242.47 Ignored Sep 7th Upstate New York Mens Club Sectional Championship 2019
27 Phoenix** Loss 1-13 927.32 Ignored Sep 7th Upstate 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)