Those metrics can be used as inputs for our neural nets and improve speed and accuracy.
In a first step we should document as many of those metrics as possible.
In later phases we then can implement them in code and try out how much benefit they bring.
Remember to no copy/paste code protected by the licenses as the GPL! Studying algorithms and then describing them in natural language is fine, however.
Previous backgammon engines have calculated various metrics to help them with their evaluation: pip counts, number of blots, etc.
Many of those metrics were introduced by Hans Berliner: https://www.semanticscholar.org/paper/BKG%E2%80%94A-Program-That-Plays-Backgammon-Berliner/da1556610fa295332aabce77533df5e773abd84d
Others were introduced by gnubg.
Those metrics can be used as inputs for our neural nets and improve speed and accuracy. In a first step we should document as many of those metrics as possible. In later phases we then can implement them in code and try out how much benefit they bring.
Remember to no copy/paste code protected by the licenses as the GPL! Studying algorithms and then describing them in natural language is fine, however.