#50 Jughandle (9-11)

avg: 1393.45  •  sd: 69.83  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
12 'Shine Win 15-8 2415.89 Jun 24th FROGS
6 Sprocket Loss 11-15 1543.8 Jun 24th FROGS
7 XIST Loss 2-15 1320.86 Jun 24th FROGS
45 Wild Card Win 15-13 1671.71 Jun 24th FROGS
65 League of Shadows Loss 12-13 1123.69 Jun 25th FROGS
3 AMP Loss 8-12 1626.7 Jul 15th TCT Pro Elite Challenge East 2023
6 Sprocket Loss 7-12 1404.45 Jul 15th TCT Pro Elite Challenge East 2023
30 Waterloo Loss 5-10 1041.41 Jul 15th TCT Pro Elite Challenge East 2023
42 The Chad Larson Experience Loss 11-13 1252.2 Jul 16th TCT Pro Elite Challenge East 2023
164 Espionage Win 14-9 1245.43 Aug 19th Philly Invite 2023
55 Garbage Plates Loss 11-13 1113.43 Aug 19th Philly Invite 2023
146 Heavy Flow Win 15-7 1423.99 Aug 19th Philly Invite 2023
38 Pittsburgh Port Authority Loss 11-14 1187.77 Aug 20th Philly Invite 2023
55 Garbage Plates Loss 10-11 1217.27 Aug 20th Philly Invite 2023
65 League of Shadows Win 12-10 1486.81 Aug 20th Philly Invite 2023
149 ColorBomb Win 13-5 1405.31 Sep 9th 2023 Mixed Founders Sectional Championship
184 Crucible** Win 13-2 1200.41 Ignored Sep 9th 2023 Mixed Founders Sectional Championship
227 The Incidentals** Win 13-3 913.98 Ignored Sep 9th 2023 Mixed Founders Sectional Championship
38 Pittsburgh Port Authority Loss 9-11 1251.9 Sep 10th 2023 Mixed Founders Sectional Championship
97 Farm Show Win 12-6 1634.4 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)