#180 SUPA FC (9-15)

avg: 789.39  •  sd: 60.87  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
186 Town Hall Stars Loss 12-14 533.34 Jun 10th SUPA FC Invite
157 Winc City Fog of War Loss 11-12 764 Jun 10th SUPA FC Invite
186 Town Hall Stars Win 12-10 992.42 Jun 10th SUPA FC Invite
157 Winc City Fog of War Loss 13-15 674.82 Jun 10th SUPA FC Invite
115 Bomb Squad Loss 9-11 895.13 Jul 8th MOB Open 2023
91 Helots Loss 7-11 827.99 Jul 8th MOB Open 2023
186 Town Hall Stars Win 12-9 1099.66 Jul 8th MOB Open 2023
161 MOB Ultimate Win 11-5 1464.78 Jul 8th MOB Open 2023
94 Magma Bears Loss 8-13 795.07 Aug 5th Philly Open 2023
157 Winc City Fog of War Loss 6-13 289 Aug 5th Philly Open 2023
252 Deepfake** Win 13-5 649.62 Ignored Aug 5th Philly Open 2023
121 John Doe Loss 5-13 489.35 Aug 6th Philly Open 2023
161 MOB Ultimate Loss 6-11 318.09 Aug 6th Philly Open 2023
186 Town Hall Stars Loss 11-13 525.45 Aug 6th Philly Open 2023
227 Brackish Loss 13-14 352.16 Aug 26th MOB Invite 2023
161 MOB Ultimate Win 12-11 989.78 Aug 26th MOB Invite 2023
177 JAWN Win 15-12 1112.93 Aug 26th MOB Invite 2023
157 Winc City Fog of War Win 14-10 1287.7 Aug 26th MOB Invite 2023
164 Pride of Rowan Newark Win 13-12 983.73 Sep 9th 2023 Mens Founders Sectional Championship
84 Pittsburgh Stealers Loss 9-13 909.11 Sep 9th 2023 Mens Founders Sectional Championship
244 Wooder Win 13-3 820.17 Sep 9th 2023 Mens Founders Sectional Championship
177 JAWN Loss 3-5 393.87 Sep 9th 2023 Mens Founders Sectional Championship
162 Delco Club Loss 14-15 738.83 Sep 16th 2023 Mens Founders Sectional Championship
84 Pittsburgh Stealers Loss 9-15 812.19 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)