#182 Anchor (2-14)

avg: 785.91  •  sd: 77.25  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
141 Make it Rain Loss 8-13 475.38 Jun 24th Summer Solstice 2023
87 Ghost Train Loss 6-13 709.41 Jun 24th Summer Solstice 2023
86 Oregon Trainwreck Loss 9-12 966.57 Jun 24th Summer Solstice 2023
193 ONI Win 13-5 1324.7 Jun 24th Summer Solstice 2023
99 SOUF Loss 7-11 788.11 Jun 25th Summer Solstice 2023
145 Green River Swordfish Loss 9-13 545.69 Jun 25th Summer Solstice 2023
11 Furious George** Loss 5-15 1457.44 Ignored Jul 15th TCT Select Flight West 2023
66 OC Crows Loss 6-9 1018.32 Jul 15th TCT Select Flight West 2023
54 ISO Atmo** Loss 6-15 919.35 Ignored Jul 15th TCT Select Flight West 2023
76 Haymaker Loss 6-15 777.16 Jul 16th TCT Select Flight West 2023
176 Battery Loss 10-11 692.3 Jul 16th TCT Select Flight West 2023
145 Green River Swordfish Loss 12-13 839.26 Sep 9th 2023 Mens Nor Cal Sectional Championship
195 Creaky Win 13-7 1270.42 Sep 9th 2023 Mens Nor Cal Sectional Championship
140 Mavericks Loss 10-12 735.48 Sep 9th 2023 Mens Nor Cal Sectional Championship
176 Battery Loss 9-10 692.3 Sep 10th 2023 Mens Nor Cal Sectional Championship
195 Creaky Loss 8-12 271.73 Sep 10th 2023 Mens Nor Cal 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)