Feint behaviors refer to a set of nuanced deceptive behaviors, which enable players temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive Multi-Player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literatures do not provide comprehensive or concrete formalization for Feint behaviors, and their implications on game strategies. In this paper, we introduce the first comprehensive formalization of Feint behaviors at action-level and strategy-level, and provide concrete implementation and quantitative evaluation in Multi-Player games. The key idea of our work is to (1) allow automatic generation of Feint behaviors via Palindrome-directed templates, and combine them with intended high-reward actions in a Dual-Behavior Model; (2) address Feint implications on game strategies in terms of the temporal, spatial and their collective impacts; and (3) provide a unified implementation scheme of Feint behaviors in existing MARL frameworks. The experimental results show that our design of Feint behaviors can (1) greatly improve the game reward gains; (2) significantly improve the diversity of Multi-Player Games; and (3) only incur negligible overheads in terms of time consumption.