With advances in statistical regression analysis, it should become feasible that lawyers today can optimize courtroom performance with the help of proficient mathematicians. Numerical examinations and linked findings could provide the average legal representatives an unconventional edge in forecasting arbiter responses.
Benefits over Conventional Practices
As seen on TV shows, movies, or with some have experienced from real life, traditional law practices mostly spend time with the represented parties scrutinizing details of entailed affairs, then look through numerous excessively thick books for relevant and possibly effective laws. Of course the parties have no idea how the trials would result, only that they had done their best, what with the appropriate homework and all.
Mathematical descriptions of judge ruling probabilities, with respect to identifiable, common variables via historical cases, could make much of research leg work obsolete. With less time wasted, law firms could take on more cases, lower research related expenses, and thereby increase revenue.
This theoretical approach would also help boost trial performance. Once quantified, historical case studies could be reviewed collectively and help prepare for future results. The numbers allow attorneys to form courtroom strategies of positive statistical expectancy.
Quantifiable Variables
As with traffic and appeal courts, the judges make the rulings. Even in jury trials, the judges have power to overturn jury recommendations. As judges tend to have long careers, it becomes workable to create regression statistics based on their historical responses.
Predictor variables include a large range of items as expected. Theoretically, everything presented by opposing counsels contributes to the final decisions of the judges. Even if not true, the regression analysis would reveal proper numerical evidence. Types of cases, special circumstances, plaintiff or defendant social status, specific attorneys, political ties, and etc. serve as but a few of many variables to take into account of.
Proposed Applications
The modeling of judge behavior functions on the viable basis that they go through consistent decision making process with each case. Actions taken by opposing legal representatives are assumed to influence the judges with equal weight. The choices via the attorneys then could represent variables of interest.
The historical pronouncements of involved judges become the “responses” of each individual case involved; and the plaintiff and defendant variables play the part of predictive points. Multiple regression analysis could then “connect the dots” and provide a statistical contribution of each variable toward particular responses of the judges.
The findings could foreshadow courtroom results with respectable accuracy. Understanding probabilities of distinct judge behavior along with historically effective tactics, attorneys could therefore perform with added confidence against those who do not possess this edge.
Reality of Uncertainty
The world does not function in black or white, but more often shades of gray. The above idea presents a potential means of a statistical edge for practicing legal counsels. Just like everything else in life, sometimes a small winning advantage could go a long way.
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