#18 Dark Star-D (17-7)

avg: 1883.29  •  sd: 46.24  •  top 16/20: 16.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
10 Rhino Slam! Loss 9-13 1667.24 Jun 24th Summer Solstice 2023
65 Sawtooth Win 13-4 2043.15 Jun 24th Summer Solstice 2023
58 Skipjack Win 13-2 2090.71 Jun 24th Summer Solstice 2023
11 Furious George Loss 9-13 1638.87 Jun 25th Summer Solstice 2023
20 Zyzzyva Win 13-10 2188.35 Jun 25th Summer Solstice 2023
14 Sockeye Win 13-12 2124.88 Jun 25th Summer Solstice 2023
3 Revolver Loss 12-15 1944.81 Jul 8th TCT Pro Elite Challenge West 2023
141 Make it Rain** Win 15-4 1571.54 Ignored Jul 8th TCT Pro Elite Challenge West 2023
70 OAT Win 15-5 1999.46 Jul 8th TCT Pro Elite Challenge West 2023
10 Rhino Slam! Loss 10-15 1632.2 Jul 9th TCT Pro Elite Challenge West 2023
29 Mallard Win 15-10 2181.87 Jul 9th TCT Pro Elite Challenge West 2023
14 Sockeye Loss 10-13 1671.73 Jul 9th TCT Pro Elite Challenge West 2023
53 Sundance Kids Win 12-10 1762.15 Aug 19th Ski Town Classic 2023
57 Fungi Win 13-7 2050.07 Aug 19th Ski Town Classic 2023
66 OC Crows Win 12-11 1561.89 Aug 19th Ski Town Classic 2023
57 Fungi Win 12-10 1730.66 Aug 20th Ski Town Classic 2023
74 Hazard Win 13-7 1940.98 Aug 20th Ski Town Classic 2023
54 ISO Atmo Win 13-8 2015.51 Aug 20th Ski Town Classic 2023
10 Rhino Slam! Loss 8-15 1520.99 Sep 23rd 2023 Northwest Mens Regional Championship
87 Ghost Train Win 15-10 1763.01 Sep 23rd 2023 Northwest Mens Regional Championship
59 Jen City Executives Win 15-8 2052.58 Sep 23rd 2023 Northwest Mens Regional Championship
10 Rhino Slam! Loss 10-15 1632.2 Sep 24th 2023 Northwest Mens Regional Championship
65 Sawtooth Win 15-8 2007.96 Sep 24th 2023 Northwest Mens Regional Championship
11 Furious George Win 15-13 2271.62 Sep 24th 2023 Northwest 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)