#41 Jughandle (9-18)

avg: 1463.84  •  sd: 61.67  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
29 Alloy Win 11-9 1871.82 Jun 9th AMP Invite 2019
3 AMP Loss 8-10 1765.48 Jun 9th AMP Invite 2019
27 Rally Loss 9-12 1291.59 Jun 9th AMP Invite 2019
10 Space Heater Win 11-10 1941.43 Jun 9th AMP Invite 2019
16 Love Tractor Loss 3-13 1127.51 Jul 13th TCT Pro Elite Challenge 2019
17 Polar Bears Loss 8-12 1268.48 Jul 13th TCT Pro Elite Challenge 2019
1 Seattle Mixtape Loss 9-12 1726.04 Jul 13th TCT Pro Elite Challenge 2019
31 NOISE Loss 10-13 1260.24 Jul 14th TCT Pro Elite Challenge 2019
5 BFG Loss 6-13 1355.14 Jul 14th TCT Pro Elite Challenge 2019
34 XIST Loss 9-10 1405.24 Jul 14th TCT Pro Elite Challenge 2019
31 NOISE Loss 11-12 1463.38 Aug 17th TCT Elite Select Challenge 2019
17 Polar Bears Loss 8-15 1144.83 Aug 17th TCT Elite Select Challenge 2019
8 Slow White Loss 11-13 1601.61 Aug 17th TCT Elite Select Challenge 2019
36 BW Ultimate Loss 4-6 1159.3 Aug 18th TCT Elite Select Challenge 2019
122 Nothing's Great Again Win 13-4 1635.85 Aug 18th TCT Elite Select Challenge 2019
6 shame. Loss 9-15 1412.53 Aug 31st TCT Pro Championships 2019
12 Snake Country Loss 9-11 1542.82 Aug 31st TCT Pro Championships 2019
13 Toro Win 13-12 1893.08 Aug 31st TCT Pro Championships 2019
3 AMP Loss 5-13 1428.15 Sep 1st TCT Pro Championships 2019
34 XIST Win 13-9 1948.81 Sep 1st TCT Pro Championships 2019
10 Space Heater Loss 7-15 1216.43 Sep 1st TCT Pro Championships 2019
89 HVAC Win 15-9 1667.94 Sep 21st Mid Atlantic Mixed Club Regional Championship 2019
15 Loco Loss 6-13 1156.06 Sep 21st Mid Atlantic Mixed Club Regional Championship 2019
115 Stoke Win 13-9 1478.68 Sep 21st Mid Atlantic Mixed Club Regional Championship 2019
110 Rat City Win 13-6 1674.3 Sep 21st Mid Atlantic Mixed Club Regional Championship 2019
87 Fleet Win 11-10 1288.71 Sep 22nd Mid Atlantic Mixed Club Regional Championship 2019
27 Rally Loss 8-15 1072.15 Sep 22nd Mid Atlantic Mixed Club Regional Championship 2019
**Blowout Eligible

FAQ

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
  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
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)