Achieving human-level performance on real world tasks is a north star for the robotics community. We present the first learned robot agent that reaches amateur human-level performance in competitive table tennis. Table tennis is a physically demanding sport that requires humans years to master. We contribute (1) a hierarchical and modular policy architecture consisting of (i) low level controllers with their skill descriptors that model their capabilities and (ii) a high level controller that chooses the low level skills, (2) techniques for enabling zero-shot sim-to-real and curriculum building, including an iterative approach (train in sim, deploy in real), and (3) real time adaptation to unseen opponents. Policy performance was assessed through 29 robot vs. human matches of which the robot won 45\% (13/29). All humans were unseen players and their skill level varied from beginner to tournament level. Whilst the robot lost all matches vs. the most advanced players it won 100\% matches vs. beginners and 55\% matches vs. intermediate players, demonstrating solidly amateur human-level performance.