#83 Red Wolves (11-12)

avg: 1347.98  •  sd: 46.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
101 Triumph Win 12-10 1450.85 Jun 25th River City Witching Hour
173 Crypt Win 15-8 1388.97 Jun 25th River City Witching Hour
138 Queen City Kings Win 10-8 1257.81 Jun 25th River City Witching Hour
39 Pittsburgh Temper Loss 8-10 1362.05 Jul 15th TCT Pro Elite Challenge East 2023
5 Chicago Machine** Loss 2-15 1589.71 Ignored Jul 15th TCT Pro Elite Challenge East 2023
28 Tanasi Loss 7-12 1211.94 Jul 15th TCT Pro Elite Challenge East 2023
25 Mad Men Loss 7-15 1157.27 Jul 16th TCT Pro Elite Challenge East 2023
21 Phoenix Loss 8-10 1596.59 Jul 29th TCT Select Flight East 2023
61 Lost Boys Loss 6-10 969.78 Jul 29th TCT Select Flight East 2023
55 Colonels Loss 9-10 1378.69 Jul 29th TCT Select Flight East 2023
46 DeMo Loss 9-10 1438.65 Jul 30th TCT Select Flight East 2023
75 Flying Dutchmen Loss 8-13 881.38 Jul 30th TCT Select Flight East 2023
110 CITYWIDE Special Win 11-10 1292.75 Jul 30th TCT Select Flight East 2023
94 Magma Bears Win 12-10 1529.35 Aug 26th The Incident 2023
82 Lantern Win 12-10 1587.84 Aug 26th The Incident 2023
24 Blueprint Loss 5-13 1164.52 Aug 26th The Incident 2023
91 Helots Loss 9-13 876.32 Aug 26th The Incident 2023
161 MOB Ultimate Win 13-3 1464.78 Sep 9th 2023 Mens Capital Sectional Championship
123 Bryce Whitney Fan Club Win 12-10 1299.23 Sep 9th 2023 Mens Capital Sectional Championship
95 Puzzles Win 13-10 1617.25 Sep 9th 2023 Mens Capital Sectional Championship
115 Bomb Squad Win 15-4 1744.34 Sep 10th 2023 Mens Capital Sectional Championship
52 Oakgrove Boys Loss 11-13 1298.84 Sep 10th 2023 Mens Capital Sectional Championship
95 Puzzles Win 15-14 1414.1 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)