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The paper

Balancing Zero-sum Games with One Variable per Strategy

Python

This is the primary implementation.

zerosum.balance package: The core functionality

This package contains the core functionality of defining handicap functions and performing the optimization that finds the balancing handicaps.

Base classes

• zerosum.balance.base

The base module contains the base classes. Implementing a handicap function is done by extending one of these base classes. See the above page for details.

Specific handicap functions

The following specific handicap functions are implemented:

• zerosum.balance.hazard
• zerosum.balance.lanchester
• zerosum.balance.logistic
• zerosum.balance.multiplicative

Note that the concrete classes (e.g. MultiplicativeBalance) are imported directly into the parent zerosum.balance package, so it is not necessary to import the specific modules; they may be accessed as e.g. zerosum.balance.MultiplicativeBalance.

examples package

Example scripts presented as Jupyter notebooks.

From the paper:

• Pokémon
• Super Street Fighter 2 Turbo
• Warhammer 40,000

Additional examples:

• Hazard
• Pokémon with dual-type defenders

Other packages and modules

• zerosum.excel: Microsoft Excel integration via user-defined functions (UDFs). Requires xlwings. Unfortunately this only works on Windows.
• zerosum.nash: Determines the Nash equilibrium of a game using linear programming.
• tests: Unit tests.

gs (Google Apps Script)

This contains a Google Apps Script implementation of Sinkhorn's algorithm, which computes handicaps for a multiplicative handicap function. An example Google Sheet running this script can be found here; make a copy to play around with it.

This does not implement any of the other handicap functions.