This study examines whether machine learning can be used to predict player performance in the Czech National Basketball League (NBL) using only information available before a game. Publicly available player- and team-level data were collected from the official NBL website and FIBA LiveStats, and the target variable was defined as Performance Index Rating (PIR), a widely used indicator of overall player efficiency in European basketball. To ensure practical usability and methodological validity, all predictor variables were constructed exclusively from pre-game information, including lagged and rolling statistics from previous matches, and explicit steps were taken to prevent data leakage. Three supervised learning models were compared: Random Forest, Extreme Gradient Boosting (XGBoost), and a simple neural network. Model performance was evaluated using R², MAE, RMSE, and MSE. Among the tested models, XGBoost achieved the best overall performance, although the predictive accuracy remained moderate rather than high, indicating that pre-game prediction of PIR is a challenging task in a smaller European league context. Feature-importance and SHAP analyses showed that prior PIR, recent form, minutes-related indicators, and selected matchup variables contributed most strongly to predictions. The study contributes to the growing literature on sports analytics by providing a transparent and reproducible pre-game prediction pipeline for Czech basketball, and the article-specific code, analytical notebook, and merged player-game dataset are made publicly available in an online repository.