#161 MOB Ultimate (9-15)

avg: 864.78  •  sd: 59.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
227 Brackish Win 15-9 992.64 Jun 25th River City Witching Hour
138 Queen City Kings Loss 12-15 694.66 Jun 25th River City Witching Hour
101 Triumph Loss 8-15 647.92 Jun 25th River City Witching Hour
180 SUPA FC Loss 5-11 189.39 Jul 8th MOB Open 2023
173 Crypt Loss 8-11 458.55 Jul 8th MOB Open 2023
52 Oakgrove Boys** Loss 2-15 927.68 Ignored Jul 8th MOB Open 2023
157 Winc City Fog of War Win 10-8 1151.67 Jul 8th MOB Open 2023
158 Alibi Win 12-11 1012.94 Aug 5th Philly Open 2023
173 Crypt Loss 10-11 699.16 Aug 5th Philly Open 2023
72 Colt Loss 8-13 893.12 Aug 5th Philly Open 2023
180 SUPA FC Win 11-6 1336.08 Aug 6th Philly Open 2023
157 Winc City Fog of War Loss 10-11 764 Aug 6th Philly Open 2023
177 JAWN Loss 8-9 687.44 Aug 6th Philly Open 2023
227 Brackish Win 15-5 1077.16 Aug 26th MOB Invite 2023
180 SUPA FC Loss 11-12 664.39 Aug 26th MOB Invite 2023
177 JAWN Win 11-7 1279.33 Aug 26th MOB Invite 2023
157 Winc City Fog of War Loss 9-12 543.64 Aug 26th MOB Invite 2023
83 Red Wolves Loss 3-13 747.98 Sep 9th 2023 Mens Capital Sectional Championship
123 Bryce Whitney Fan Club Win 13-11 1289.95 Sep 9th 2023 Mens Capital Sectional Championship
52 Oakgrove Boys** Loss 5-14 927.68 Ignored Sep 9th 2023 Mens Capital Sectional Championship
115 Bomb Squad Loss 3-15 544.34 Sep 10th 2023 Mens Capital Sectional Championship
123 Bryce Whitney Fan Club Loss 10-14 662.41 Sep 10th 2023 Mens Capital Sectional Championship
157 Winc City Fog of War Win 11-10 1014 Sep 10th 2023 Mens Capital Sectional Championship
157 Winc City Fog of War Win 13-9 1307.57 Sep 10th 2023 Mens Capital 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)