#65 Sawtooth (10-13)

avg: 1443.15  •  sd: 43.61  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
10 Rhino Slam!** Loss 4-13 1485.8 Ignored Jun 24th Summer Solstice 2023
18 Dark Star-D Loss 4-13 1283.29 Jun 24th Summer Solstice 2023
58 Skipjack Loss 11-13 1261.87 Jun 24th Summer Solstice 2023
33 Blackfish Loss 9-13 1248.02 Jun 25th Summer Solstice 2023
86 Oregon Trainwreck Loss 9-13 893.37 Jun 25th Summer Solstice 2023
87 Ghost Train Win 13-9 1727.97 Jun 25th Summer Solstice 2023
68 Brawl Win 10-8 1681.22 Aug 19th Ski Town Classic 2023
54 ISO Atmo Loss 8-11 1153.74 Aug 19th Ski Town Classic 2023
145 Green River Swordfish Win 13-5 1564.26 Aug 19th Ski Town Classic 2023
57 Fungi Loss 12-13 1367.54 Aug 20th Ski Town Classic 2023
66 OC Crows Loss 12-13 1311.89 Aug 20th Ski Town Classic 2023
74 Hazard Win 10-9 1508.45 Aug 20th Ski Town Classic 2023
53 Sundance Kids Win 15-12 1824.52 Sep 9th 2023 Mens Big Sky Sectional Championship
128 PowderHogs Win 14-12 1269.45 Sep 9th 2023 Mens Big Sky Sectional Championship
113 Utah Hatu Win 15-7 1757.14 Sep 10th 2023 Mens Big Sky Sectional Championship
49 Shrimp Loss 13-15 1341.05 Sep 10th 2023 Mens Big Sky Sectional Championship
11 Furious George Loss 9-15 1541.96 Sep 23rd 2023 Northwest Mens Regional Championship
49 Shrimp Loss 14-15 1430.23 Sep 23rd 2023 Northwest Mens Regional Championship
67 Mystery Gang Win 15-14 1550.91 Sep 23rd 2023 Northwest Mens Regional Championship
18 Dark Star-D Loss 8-15 1318.48 Sep 24th 2023 Northwest Mens Regional Championship
81 Surf Win 15-13 1564.15 Sep 24th 2023 Northwest Mens Regional Championship
14 Sockeye Loss 7-15 1399.88 Sep 24th 2023 Northwest Mens Regional Championship
99 SOUF Win 15-14 1380.01 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)