This chapter explores emerging analytical approaches and future directions in the study of police misconduct, moving beyond static, cross-sectional accounts to more dynamic, relational, and developmental perspectives. It argues that while misconduct, at times, features aberrant behaviour, it is better understood as a phenomenon shaped by group dynamics, career trajectories, organisational contexts, and broader social environments. The chapter highlights network perspectives, showing how misconduct clusters within peer groups, spreads through social influence, and is sustained by protective norms. Longitudinal and time-series approaches are presented as tools for tracing misconduct over time, identifying onset points, escalation, desistance, and the influence of career milestones or organisational change. Machine learning approaches extend analytical capacity by uncovering complex patterns in large datasets, detecting hidden risk factors, and improving predictive accuracy, though challenges of bias and transparency remain. Life-course perspectives further situate misconduct within a broader personal and professional context, emphasising the role of stressors and cumulative experiences in shaping risk. Together, these approaches can offer a new era of misconduct research, combining methodological sophistication with applied insight, enabling more precise and timely interventions to enhance police integrity.