#91 Helots (13-6)

avg: 1294.88  •  sd: 60.01  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
115 Bomb Squad Loss 9-11 895.13 Jul 8th MOB Open 2023
180 SUPA FC Win 11-7 1256.28 Jul 8th MOB Open 2023
186 Town Hall Stars Win 10-8 1016.96 Jul 8th MOB Open 2023
173 Crypt Win 8-7 949.16 Jul 8th MOB Open 2023
200 Rochester Open Club** Win 13-4 1282.18 Ignored Aug 5th Philly Open 2023
186 Town Hall Stars Win 13-9 1172.86 Aug 5th Philly Open 2023
121 John Doe Win 11-7 1556.25 Aug 5th Philly Open 2023
80 Rumspringa Loss 9-12 1020.24 Aug 6th Philly Open 2023
133 BAG Win 10-5 1608.2 Aug 6th Philly Open 2023
115 Bomb Squad Win 9-8 1269.34 Aug 6th Philly Open 2023
83 Red Wolves Win 13-9 1766.55 Aug 26th The Incident 2023
52 Oakgrove Boys Loss 9-12 1182.32 Aug 26th The Incident 2023
94 Magma Bears Loss 5-11 691.23 Aug 26th The Incident 2023
82 Lantern Loss 10-12 1111.59 Aug 26th The Incident 2023
235 Adelphos** Win 7-1 974.59 Ignored Sep 9th 2023 Mens Founders Sectional Championship
31 Garden State Ultimate Loss 10-12 1438 Sep 9th 2023 Mens Founders Sectional Championship
162 Delco Club Win 13-7 1421.36 Sep 9th 2023 Mens Founders Sectional Championship
160 Happy Valley Roofing and Mustache Supply Win 13-5 1471.09 Sep 9th 2023 Mens Founders Sectional Championship
84 Pittsburgh Stealers Win 15-11 1708.84 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)