#58 Skipjack (13-11)

avg: 1490.71  •  sd: 52.27  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
10 Rhino Slam! Loss 3-13 1485.8 Jun 24th Summer Solstice 2023
65 Sawtooth Win 13-11 1671.99 Jun 24th Summer Solstice 2023
18 Dark Star-D Loss 2-13 1283.29 Jun 24th Summer Solstice 2023
33 Blackfish Loss 8-9 1541.59 Jun 25th Summer Solstice 2023
60 Switchback Loss 11-13 1246.93 Jun 25th Summer Solstice 2023
11 Furious George Loss 3-13 1457.44 Jun 25th Summer Solstice 2023
112 Heartbreak Win 13-7 1714.82 Jun 25th Summer Solstice 2023
87 Ghost Train Win 11-6 1856.1 Jul 15th TCT Select Flight West 2023
36 Kansas City Smokestack Loss 9-15 1126.41 Jul 15th TCT Select Flight West 2023
171 Sonoran Dog** Win 13-4 1428.2 Ignored Jul 15th TCT Select Flight West 2023
74 Hazard Win 10-7 1773.12 Jul 16th TCT Select Flight West 2023
54 ISO Atmo Loss 12-15 1218.86 Jul 16th TCT Select Flight West 2023
99 SOUF Win 15-7 1855.01 Jul 16th TCT Select Flight West 2023
78 Drought Loss 12-13 1249.11 Sep 9th 2023 Mens So Cal Sectional Championship
196 Casino Ultimate** Win 13-5 1308.12 Ignored Sep 9th 2023 Mens So Cal Sectional Championship
242 Monsoon** Win 13-3 847.9 Ignored Sep 9th 2023 Mens So Cal Sectional Championship
171 Sonoran Dog Win 13-6 1428.2 Sep 9th 2023 Mens So Cal Sectional Championship
78 Drought Loss 8-10 1111.44 Sep 10th 2023 Mens So Cal Sectional Championship
66 OC Crows Win 13-10 1765.03 Sep 10th 2023 Mens So Cal Sectional Championship
78 Drought Win 14-11 1687.45 Sep 23rd 2023 Southwest Mens Regional Championship
140 Mavericks Win 13-10 1301.75 Sep 23rd 2023 Southwest Mens Regional Championship
3 Revolver Loss 7-15 1645.3 Sep 24th 2023 Southwest Mens Regional Championship
70 OAT Win 15-14 1524.46 Sep 24th 2023 Southwest Mens Regional Championship
22 SoCal Condors Loss 11-15 1471.24 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)