experiment-playing-time

Automating feature engineering for playing-time projections involves running an experimentation loop that tests features, diagnoses model weaknesses, and generates hypotheses for batters and pitchers.

Yes, playing-time experiments support both batters and pitchers by allowing you to specify the player type when running the automated feature testing loop.

You can limit iterations by specifying an optional budget parameter, such as '--budget 15', to constrain the automated feature discovery loop to 15 experiments.

Automated hypothesis testing in playing-time forecasting systematically diagnoses predictive model weaknesses, generates potential feature improvements, logs results, and presents a summary report.

No, the playing-time experimentation skill operates entirely through its internal scripts and requires no external dependencies to execute automated feature experiments.

Regression analysis improves playing-time models by systematically testing engineered features against historical data to identify variables that increase predictive accuracy for players.