The Scavenging Guide - The Future of Early Game Farming

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mn94

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Please find an analysis of the scavenge feature in the following jupyter notebook:
https://nbviewer.jupyter.org/github/mikenoethiger/tw-scavenge/blob/master/scavenge.ipynb

The notebook includes python code for computing return (resources), duration (seconds) and performance (resources/second) of a scavenge run. Moreover, a method for calculating optimal distribution of given capacity to level 1,2,3,4 scavenge in order to reach maximum performance is presented. Rather than solving the problem analytic, an algorithmic approach (using scipy.optimize.minimize function) was used to do the job.

The notebook is also available on GitHub.

Conclusion: Sending an equal amount of units in every level is almost always the most profitable approach.
 

JawJaw

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Tribal Wars Team
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Please find an analysis of the scavenge feature in the following jupyter notebook:
https://nbviewer.jupyter.org/github/mikenoethiger/tw-scavenge/blob/master/scavenge.ipynb

The notebook includes python code for computing return (resources), duration (seconds) and performance (resources/second) of a scavenge run. Moreover, a method for calculating optimal distribution of given capacity to level 1,2,3,4 scavenge in order to reach maximum performance is presented. Rather than solving the problem analytic, an algorithmic approach (using scipy.optimize.minimize function) was used to do the job.

The notebook is also available on GitHub.

Conclusion: Sending an equal amount of units in every level is almost always the most profitable approach.
Love this. Great work!
 

Nongbu

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Please find an analysis of the scavenge feature in the following jupyter notebook:
https://nbviewer.jupyter.org/github/mikenoethiger/tw-scavenge/blob/master/scavenge.ipynb

The notebook includes python code for computing return (resources), duration (seconds) and performance (resources/second) of a scavenge run. Moreover, a method for calculating optimal distribution of given capacity to level 1,2,3,4 scavenge in order to reach maximum performance is presented. Rather than solving the problem analytic, an algorithmic approach (using scipy.optimize.minimize function) was used to do the job.

The notebook is also available on GitHub.

Conclusion: Sending an equal amount of units in every level is almost always the most profitable approach.
Wow are you statistician lol nice work