#24 Loco (14-7)

avg: 1670.85  •  sd: 74.07  •  top 16/20: 5.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
19 Public Enemy Loss 9-10 1613.53 Jul 15th TCT Pro Elite Challenge East 2023
29 RAMP Loss 9-11 1373.54 Jul 15th TCT Pro Elite Challenge East 2023
20 Toro Loss 9-15 1222 Jul 15th TCT Pro Elite Challenge East 2023
14 Rally Win 14-9 2307.61 Jul 16th TCT Pro Elite Challenge East 2023
201 Spice** Win 13-2 1123.93 Ignored Aug 5th Philly Open 2023
155 NY Swipes** Win 13-3 1390.42 Ignored Aug 5th Philly Open 2023
142 Goosebumps** Win 13-4 1443.61 Ignored Aug 5th Philly Open 2023
164 Espionage** Win 13-3 1371.56 Ignored Aug 6th Philly Open 2023
22 Storm Loss 10-11 1564.35 Aug 6th Philly Open 2023
119 Mashed** Win 13-4 1583.99 Ignored Aug 6th Philly Open 2023
10 Red Flag Loss 9-13 1457.65 Aug 26th Northwest Fruit Bowl 2023
36 BW Ultimate Win 9-6 1929.78 Aug 26th Northwest Fruit Bowl 2023
45 Wild Card Win 13-8 1953.69 Aug 26th Northwest Fruit Bowl 2023
11 Seattle Mixtape Loss 12-13 1749.03 Aug 27th Northwest Fruit Bowl 2023
23 Oregon Scorch Loss 10-12 1438.63 Aug 27th Northwest Fruit Bowl 2023
32 Mile High Trash Win 12-10 1800.97 Aug 27th Northwest Fruit Bowl 2023
149 ColorBomb Win 13-6 1405.31 Sep 9th 2023 Mixed Founders Sectional Championship
184 Crucible** Win 13-4 1200.41 Ignored Sep 9th 2023 Mixed Founders Sectional Championship
227 The Incidentals** Win 13-4 913.98 Ignored Sep 9th 2023 Mixed Founders Sectional Championship
38 Pittsburgh Port Authority Win 10-7 1890.77 Sep 10th 2023 Mixed Founders Sectional Championship
97 Farm Show** Win 13-4 1655.09 Ignored Sep 10th 2023 Mixed Founders 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)