#186 Town Hall Stars (4-14)

avg: 754.3  •  sd: 61.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
180 SUPA FC Win 14-12 1010.34 Jun 10th SUPA FC Invite
180 SUPA FC Loss 10-12 551.26 Jun 10th SUPA FC Invite
157 Winc City Fog of War Loss 9-12 543.64 Jun 10th SUPA FC Invite
115 Bomb Squad Loss 7-10 754.67 Jul 8th MOB Open 2023
180 SUPA FC Loss 9-12 444.02 Jul 8th MOB Open 2023
91 Helots Loss 8-10 1032.22 Jul 8th MOB Open 2023
157 Winc City Fog of War Loss 8-12 447.85 Jul 8th MOB Open 2023
200 Rochester Open Club Win 12-9 1027.54 Aug 5th Philly Open 2023
91 Helots Loss 9-13 876.32 Aug 5th Philly Open 2023
121 John Doe Loss 7-9 810.02 Aug 5th Philly Open 2023
180 SUPA FC Win 13-11 1018.23 Aug 6th Philly Open 2023
117 Chimney Loss 7-13 562.04 Aug 6th Philly Open 2023
177 JAWN Loss 6-9 393.87 Aug 6th Philly Open 2023
52 Oakgrove Boys Loss 10-13 1199.54 Sep 9th 2023 Mens Capital Sectional Championship
95 Puzzles Loss 5-13 689.1 Sep 9th 2023 Mens Capital Sectional Championship
223 Beef Depot Win 14-7 1075.37 Sep 10th 2023 Mens Capital Sectional Championship
157 Winc City Fog of War Loss 9-14 415.13 Sep 10th 2023 Mens Capital Sectional Championship
183 Bearfax Loss 9-11 533.79 Sep 10th 2023 Mens Capital Sectional Championship
**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)