#177 JAWN (6-10)

avg: 812.44  •  sd: 59.01  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
133 BAG Loss 8-9 909.3 Aug 5th Philly Open 2023
115 Bomb Squad Loss 6-8 843.84 Aug 5th Philly Open 2023
209 Long Island Riff Raff Win 13-5 1189.54 Aug 5th Philly Open 2023
186 Town Hall Stars Win 9-6 1172.86 Aug 6th Philly Open 2023
161 MOB Ultimate Win 9-8 989.78 Aug 6th Philly Open 2023
173 Crypt Loss 8-9 699.16 Aug 6th Philly Open 2023
227 Brackish Win 15-10 930.76 Aug 26th MOB Invite 2023
180 SUPA FC Loss 12-15 488.89 Aug 26th MOB Invite 2023
161 MOB Ultimate Loss 7-11 397.89 Aug 26th MOB Invite 2023
157 Winc City Fog of War Loss 14-15 764 Aug 26th MOB Invite 2023
180 SUPA FC Win 5-3 1207.95 Sep 9th 2023 Mens Founders Sectional Championship
84 Pittsburgh Stealers Loss 6-13 727.67 Sep 9th 2023 Mens Founders Sectional Championship
244 Wooder Win 13-5 820.17 Sep 9th 2023 Mens Founders Sectional Championship
164 Pride of Rowan Newark Loss 11-13 629.89 Sep 9th 2023 Mens Founders Sectional Championship
31 Garden State Ultimate** Loss 5-15 1076.12 Ignored Sep 16th 2023 Mens Founders Sectional Championship
162 Delco Club Loss 11-12 738.83 Sep 16th 2023 Mens Founders Sectional Championship
**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)