#211 Bearproof (4-13)

avg: 391.47  •  sd: 65.27  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
50 Colt** Loss 5-13 744.87 Ignored Aug 3rd Philly Open 2019
172 Hazard Loss 11-13 427.44 Aug 3rd Philly Open 2019
230 Hot Tamales Win 13-6 794.75 Aug 3rd Philly Open 2019
152 Watchdogs Loss 11-13 534.06 Aug 3rd Philly Open 2019
220 Genny Lite Win 13-7 830.62 Aug 4th Philly Open 2019
137 Babe Loss 6-13 241.9 Aug 4th Philly Open 2019
96 Magma Bears** Loss 4-13 468.2 Ignored Aug 10th Nuccis Cup 2019
207 Sky Hook Loss 8-10 156.96 Aug 10th Nuccis Cup 2019
131 Slag Dump Loss 2-13 292.66 Aug 10th Nuccis Cup 2019
107 John Doe Loss 6-13 406.94 Aug 10th Nuccis Cup 2019
207 Sky Hook Win 15-13 633.8 Aug 11th Nuccis Cup 2019
199 Winc City Fog of War Loss 10-15 11 Aug 11th Nuccis Cup 2019
38 Garden State Ultimate** Loss 0-13 855.02 Ignored Sep 7th Founders Mens Club Sectional Championship 2019
172 Hazard Loss 11-13 427.44 Sep 7th Founders Mens Club Sectional Championship 2019
171 Adelphos Loss 7-13 99.27 Sep 7th Founders Mens Club Sectional Championship 2019
216 Apollo 7 Loss 11-13 112.82 Sep 7th Founders Mens Club Sectional Championship 2019
236 Space Force Win 13-4 622.25 Sep 8th Founders 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)