Given the challenges of modeling multi-scale social phenomena, hybrids may hold the key to unlocking social complexity dynamics. We introduce hybrid system modeling from engineering, as a means to capture complex dynamics within interacting, multi-scale, and global social systems. Whereby hybrid modeling is used in industrial processes and automated control systems, this research uses world cup soccer tournament simulations to demonstrate successful applications. Agent-based modeling for soccer games and cellular automatons for crowd and bettor emotional reactions are modeled on each side of a playing field. A predator-prey theoretical approach is applied with self-organizing soccer teams represented as predators and the soccer ball as prey. Simulations of multiple soccer tournaments of thirty-two teams were conducted with pre-game betting and without betting as a pseudo-control measure. Tournaments conducted with pre-game betting resulted in the final tournament games having the wining team demonstrating strong defensive playing styles and scoring by a large margin. Divergence of playing styles did not develop in tournaments without pre-game betting. Hybrids offer a means to explore complexity with evolutionary learning by players, corresponding emotional reactions of spectators, and betting interacting, resulting in patterns of emergent behavior and unique evolutionary behavioral responses to complexity.