#59 Grit City (14-5)

avg: 1312.32  •  sd: 67.45  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
47 Donuts Loss 5-9 914.32 Jul 8th Revolution 2023
113 Shipwreck Win 9-4 1621.03 Jul 8th Revolution 2023
172 Nebula Win 15-2 1336.26 Jul 8th Revolution 2023
88 Mango Loss 8-9 1000.86 Jul 8th Revolution 2023
60 Cutthroat Win 12-10 1548.4 Jul 9th Revolution 2023
179 VU Win 12-6 1251.08 Jul 9th Revolution 2023
91 Hive Win 12-6 1682.71 Jul 9th Revolution 2023
190 Drip** Win 11-4 1205.37 Ignored Aug 12th Kleinman Eruption 2023
200 Surge** Win 11-3 1149.77 Ignored Aug 12th Kleinman Eruption 2023
168 Choco Ghost House Win 9-7 1021.44 Aug 12th Kleinman Eruption 2023
172 Nebula Win 10-4 1336.26 Aug 12th Kleinman Eruption 2023
79 Bullet Train Loss 5-8 724.64 Aug 13th Kleinman Eruption 2023
172 Nebula Win 13-7 1293.79 Aug 13th Kleinman Eruption 2023
9 Red Flag Loss 9-13 1523.01 Sep 9th 2023 Mixed Washington Sectional Championship
77 Seattle Soft Serve Win 14-13 1313.81 Sep 9th 2023 Mixed Washington Sectional Championship
109 Garbage Win 15-6 1625.87 Sep 9th 2023 Mixed Washington Sectional Championship
125 TT Loss 10-12 738.22 Sep 9th 2023 Mixed Washington Sectional Championship
114 Squid Inc. Win 13-6 1618.92 Sep 10th 2023 Mixed Washington Sectional Championship
125 TT Win 12-6 1555.65 Sep 10th 2023 Mixed Washington 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)