#22 SoCal Condors (9-8)

avg: 1852.4  •  sd: 64.35  •  top 16/20: 11.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
48 Alamode Win 15-7 2156.94 Jul 8th TCT Pro Elite Challenge West 2023
4 Chain Lightning Loss 11-14 1898.55 Jul 8th TCT Pro Elite Challenge West 2023
29 Mallard Win 15-4 2328.26 Jul 8th TCT Pro Elite Challenge West 2023
3 Revolver Loss 11-15 1864.13 Jul 9th TCT Pro Elite Challenge West 2023
10 Rhino Slam! Win 11-10 2210.8 Jul 9th TCT Pro Elite Challenge West 2023
14 Sockeye Win 12-10 2238 Jul 9th TCT Pro Elite Challenge West 2023
7 DiG Loss 10-15 1713.91 Aug 19th TCT Elite Select Challenge 2023
31 Garden State Ultimate Loss 12-14 1455.16 Aug 19th TCT Elite Select Challenge 2023
34 Trident I Win 13-10 1989.95 Aug 19th TCT Elite Select Challenge 2023
4 Chain Lightning Loss 10-15 1758.28 Aug 20th TCT Elite Select Challenge 2023
20 Zyzzyva Loss 12-13 1735.21 Aug 20th TCT Elite Select Challenge 2023
14 Sockeye Loss 7-14 1416.99 Aug 20th TCT Elite Select Challenge 2023
145 Green River Swordfish** Win 15-4 1564.26 Ignored Sep 23rd 2023 Southwest Mens Regional Championship
70 OAT Win 15-9 1914.94 Sep 23rd 2023 Southwest Mens Regional Championship
74 Hazard Win 15-5 1983.45 Sep 24th 2023 Southwest Mens Regional Championship
20 Zyzzyva Loss 12-15 1559.72 Sep 24th 2023 Southwest Mens Regional Championship
58 Skipjack Win 15-11 1871.87 Sep 24th 2023 Southwest Mens Regional 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)