#145 Green River Swordfish (9-12)

avg: 964.26  •  sd: 66.57  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
33 Blackfish Loss 8-13 1170.43 Jun 24th Summer Solstice 2023
99 SOUF Win 13-8 1751.17 Jun 24th Summer Solstice 2023
112 Heartbreak Loss 7-13 599.75 Jun 24th Summer Solstice 2023
182 Anchor Win 13-9 1204.48 Jun 25th Summer Solstice 2023
141 Make it Rain Loss 11-13 742.7 Jun 25th Summer Solstice 2023
193 ONI Win 11-10 849.7 Jun 25th Summer Solstice 2023
65 Sawtooth Loss 5-13 843.15 Aug 19th Ski Town Classic 2023
74 Hazard Loss 6-10 887.29 Aug 19th Ski Town Classic 2023
171 Sonoran Dog Win 13-8 1324.36 Aug 19th Ski Town Classic 2023
78 Drought Loss 6-13 774.11 Aug 20th Ski Town Classic 2023
171 Sonoran Dog Win 11-10 953.2 Aug 20th Ski Town Classic 2023
113 Utah Hatu Loss 10-13 829 Aug 20th Ski Town Classic 2023
182 Anchor Win 13-12 910.91 Sep 9th 2023 Mens Nor Cal Sectional Championship
195 Creaky Win 13-12 837.89 Sep 9th 2023 Mens Nor Cal Sectional Championship
70 OAT Loss 13-15 1185.28 Sep 9th 2023 Mens Nor Cal Sectional Championship
188 Sauce Win 2-1 873.82 Sep 10th 2023 Mens Nor Cal Sectional Championship
140 Mavericks Loss 7-12 453.09 Sep 10th 2023 Mens Nor Cal Sectional Championship
22 SoCal Condors** Loss 4-15 1252.4 Ignored Sep 23rd 2023 Southwest Mens Regional Championship
70 OAT Loss 6-15 799.46 Sep 23rd 2023 Southwest Mens Regional Championship
66 OC Crows Loss 7-15 836.89 Sep 23rd 2023 Southwest Mens Regional Championship
140 Mavericks Win 15-5 1573.6 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)