#41 Heist (18-4)

avg: 1041.1  •  sd: 77.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
102 Solstice** Win 13-4 281.02 Ignored Jul 8th Heavyweights 2023
54 Stellar Win 13-4 1476.94 Jul 8th Heavyweights 2023
81 Stormborn** Win 13-5 891.05 Ignored Jul 8th Heavyweights 2023
76 Medusa Win 13-6 1042.22 Jul 8th Heavyweights 2023
60 Wicked Win 13-7 1280.45 Jul 9th Heavyweights 2023
92 Sureshot** Win 13-2 647.83 Ignored Jul 9th Heavyweights 2023
34 Indy Rogue Loss 7-13 628.81 Jul 9th Heavyweights 2023
62 Dish Win 12-9 1048.15 Jul 29th TCT Select Flight East 2023
80 Notorious C.L.E.** Win 15-1 902.98 Ignored Jul 29th TCT Select Flight East 2023
22 Siege Loss 4-13 941.48 Jul 29th TCT Select Flight East 2023
32 Crush City Loss 8-14 777.36 Jul 30th TCT Select Flight East 2023
18 Starling Ultimate** Loss 4-15 1074.5 Ignored Jul 30th TCT Select Flight East 2023
54 Stellar Win 12-10 1115.06 Jul 30th TCT Select Flight East 2023
62 Dish Win 13-5 1302.78 Aug 19th Cooler Classic 34
81 Stormborn Win 13-7 848.58 Aug 19th Cooler Classic 34
104 Minnesota Superior B** Win 15-1 45.49 Ignored Aug 19th Cooler Classic 34
93 Freshwater Ultimate** Win 15-3 645.38 Ignored Aug 20th Cooler Classic 34
97 Minnesota Superior A** Win 15-4 576.66 Ignored Aug 20th Cooler Classic 34
76 Medusa Win 10-5 1016.12 Aug 20th Cooler Classic 34
93 Freshwater Ultimate** Win 15-2 645.38 Ignored Sep 9th 2023 Womens Northwest Plains Sectional Championship
81 Stormborn** Win 15-2 891.05 Ignored Sep 9th 2023 Womens Northwest Plains Sectional Championship
76 Medusa Win 15-7 1042.22 Sep 9th 2023 Womens Northwest Plains 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)