#173 Crypt (9-9)

avg: 824.16  •  sd: 80.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
83 Red Wolves Loss 8-15 783.17 Jun 25th River City Witching Hour
227 Brackish Win 13-7 1034.69 Jun 25th River City Witching Hour
138 Queen City Kings Win 15-9 1510.63 Jun 25th River City Witching Hour
161 MOB Ultimate Win 11-8 1230.39 Jul 8th MOB Open 2023
91 Helots Loss 7-8 1169.88 Jul 8th MOB Open 2023
157 Winc City Fog of War Win 10-9 1014 Jul 8th MOB Open 2023
52 Oakgrove Boys** Loss 3-12 927.68 Ignored Jul 8th MOB Open 2023
158 Alibi Win 12-5 1487.94 Aug 5th Philly Open 2023
161 MOB Ultimate Win 11-10 989.78 Aug 5th Philly Open 2023
72 Colt Loss 3-13 789.28 Aug 5th Philly Open 2023
177 JAWN Win 9-8 937.44 Aug 6th Philly Open 2023
157 Winc City Fog of War Loss 6-9 470.44 Aug 6th Philly Open 2023
117 Chimney Loss 6-9 701.01 Aug 6th Philly Open 2023
223 Beef Depot Loss 9-10 367.48 Sep 9th 2023 Mens Capital Sectional Championship
227 Brackish Win 8-7 602.16 Sep 9th 2023 Mens Capital Sectional Championship
183 Bearfax Loss 8-13 286.84 Sep 9th 2023 Mens Capital Sectional Championship
227 Brackish Win 12-8 918.31 Sep 10th 2023 Mens Capital Sectional Championship
123 Bryce Whitney Fan Club Loss 7-12 540.6 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)