#171 Sonoran Dog (7-18)

avg: 828.2  •  sd: 49.9  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
54 ISO Atmo Loss 7-13 961.82 Jun 24th Colorado Summer Solstice 2023
128 PowderHogs Loss 6-11 501.8 Jun 24th Colorado Summer Solstice 2023
159 Choice City Hops Loss 7-11 417.75 Jun 24th Colorado Summer Solstice 2023
184 False Summit Loss 9-13 364.32 Jun 25th Colorado Summer Solstice 2023
224 Mycellium Win 13-8 986.23 Jun 25th Colorado Summer Solstice 2023
131 NOx Win 10-8 1301.28 Jun 25th Colorado Summer Solstice 2023
76 Haymaker Loss 6-15 777.16 Jul 15th TCT Select Flight West 2023
70 OAT Loss 6-13 799.46 Jul 15th TCT Select Flight West 2023
58 Skipjack** Loss 4-13 890.71 Ignored Jul 15th TCT Select Flight West 2023
87 Ghost Train Loss 6-11 762.71 Jul 16th TCT Select Flight West 2023
66 OC Crows Loss 11-12 1311.89 Jul 16th TCT Select Flight West 2023
142 Fat Stacks Loss 8-10 706.56 Aug 19th Ski Town Classic 2023
145 Green River Swordfish Loss 8-13 468.1 Aug 19th Ski Town Classic 2023
49 Shrimp** Loss 3-13 955.23 Ignored Aug 19th Ski Town Classic 2023
68 Brawl Loss 5-13 818.55 Aug 20th Ski Town Classic 2023
145 Green River Swordfish Loss 10-11 839.26 Aug 20th Ski Town Classic 2023
78 Drought Loss 4-13 774.11 Sep 9th 2023 Mens So Cal Sectional Championship
196 Casino Ultimate Win 13-12 833.12 Sep 9th 2023 Mens So Cal Sectional Championship
242 Monsoon Win 11-8 613.51 Sep 9th 2023 Mens So Cal Sectional Championship
58 Skipjack Loss 6-13 890.71 Sep 9th 2023 Mens So Cal Sectional Championship
220 Las Vegas Nuke Win 13-8 1021.21 Sep 10th 2023 Mens So Cal Sectional Championship
74 Hazard Loss 7-15 783.45 Sep 23rd 2023 Southwest Mens Regional Championship
104 Offshore Loss 2-15 592.17 Sep 23rd 2023 Southwest Mens Regional Championship
176 Battery Win 13-9 1235.87 Sep 24th 2023 Southwest Mens Regional Championship
140 Mavericks Win 9-8 1098.6 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)