#21 Love Tractor (14-10)

avg: 1707.03  •  sd: 59.94  •  top 16/20: 6.9%

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# Opponent Result Game Rating Status Date Event
197 Springs Mixed Greens** Win 15-3 1141.33 Ignored Jun 24th Colorado Summer Solstice 2023
35 Impact Win 10-7 1906.76 Jun 24th Colorado Summer Solstice 2023
139 Karma** Win 15-4 1451.96 Ignored Jun 24th Colorado Summer Solstice 2023
41 California Burrito Win 13-8 1984.38 Jun 25th Colorado Summer Solstice 2023
35 Impact Loss 10-12 1278.97 Jun 25th Colorado Summer Solstice 2023
31 Kansas City United Loss 10-12 1357.03 Jun 25th Colorado Summer Solstice 2023
9 Space Force Loss 8-13 1394.54 Aug 19th TCT Elite Select Challenge 2023
15 Mischief Loss 11-12 1705.88 Aug 19th TCT Elite Select Challenge 2023
35 Impact Win 14-9 1990.96 Aug 19th TCT Elite Select Challenge 2023
12 'Shine Loss 11-14 1537.75 Aug 20th TCT Elite Select Challenge 2023
42 The Chad Larson Experience Win 12-10 1719.16 Aug 20th TCT Elite Select Challenge 2023
20 Toro Win 11-10 1862.48 Aug 20th TCT Elite Select Challenge 2023
2 Drag'n Thrust Loss 8-15 1507.37 Sep 2nd TCT Pro Championships 2023
18 Polar Bears Loss 6-15 1161.21 Sep 2nd TCT Pro Championships 2023
4 BFG Win 11-10 2084.6 Sep 2nd TCT Pro Championships 2023
7 XIST Loss 9-14 1446.99 Sep 2nd TCT Pro Championships 2023
13 Slow Win 12-10 2087.71 Sep 3rd TCT Pro Championships 2023
18 Polar Bears Win 11-9 2010.42 Sep 3rd TCT Pro Championships 2023
7 XIST Loss 8-11 1555.25 Sep 4th TCT Pro Championships 2023
85 Risky Business Win 12-7 1646.98 Sep 23rd 2023 South Central Mixed Regional Championship
142 Goosebumps** Win 15-3 1443.61 Ignored Sep 23rd 2023 South Central Mixed Regional Championship
19 Public Enemy Win 12-9 2083.89 Sep 24th 2023 South Central Mixed Regional Championship
1 shame. Loss 9-15 1651.73 Sep 24th 2023 South Central Mixed Regional Championship
102 Space Ghosts** Win 15-4 1638.16 Ignored Sep 24th 2023 South Central Mixed Regional 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)