The document discusses the development of Matbench, a standardized benchmark for evaluating machine learning algorithms for materials property prediction. Matbench includes 13 standardized datasets covering a variety of materials prediction tasks. It employs a nested cross-validation procedure to evaluate algorithms and ranks submissions on an online leaderboard. This allows for reproducible evaluation and comparison of different algorithms. Matbench has provided insights into which algorithm types work best for certain prediction problems and has helped measure overall progress in the field. Future work aims to expand Matbench with more diverse datasets and evaluation procedures to better represent real-world materials design challenges.