(Visit here for my first look at 170.6.)
Everyone knows that a lawyer must understand the legal thresholds applicable to their cases. However, it is just as important that a lawyer understands their judge—their background, their preferences, their tendencies. This is the information through which a lawyer can map an effective litigation strategy, a strategy that includes careful consideration of whether or not to “ding” a particular judge, meaning request assignment to a different judge through a CCP 170.6 peremptory challenge.
Between 2006 and 2018, the largest percentage of CCP 170.6 challenges filed in the Superior Court of Los Angeles County involved wrongful termination and other employment claims.
There are at least two ways we can perform a closer study of this diagram. We might, for example, add a temporal dimension to this data.
This move highlights the extent to which wrongful termination cases did not always come with the most CCP 170.6 challenges in Los Angeles County. In fact, in 2009, intellectual property claims elicited the largest percentage of peremptory challenges against judges.
We can also direct our attention to the parties issuing CCP 170.6 challenges.
Within the Superior Court of Los Angeles, more than half of all CCP 170.6 challenges have been filed by plaintiffs, with plaintiffs filing the highest percentage of CCP 170.6 challenges in writ and breach of contract claims.
These analyses underscore an interesting dynamic. The data presented thus far tell us little about the individual judges that have presided over cases in Los Angeles County. They tell us much more about how lawyers have acted on their own judicial inferences.
Let’s move to the Superior Court of Napa County. What can we learn about the judges that have presided throughout this county?
Between 2006 and 2018, breach of contract claims garnered the highest percentage of CCP 170.6 challenges in Napa County.
Yet despite facing the highest volume of peremptory challenges, as a whole, the five judges assigned to the most breach of contract cases have ruled fairly consistently between 2006 and 2018. Each judge has granted 70 to 71 percent of the causes of action (in breach of contract filings) over which they have presided.
A similar story emerges when it comes to demurrers in breach of contract filings. These same judges have shared similar ruling tendencies, overruling demurrers in 40 to 50 percent of filings. (Yet we can see that Judge C is a slight outlier. This judge has had the highest percentage of overruled demurrers and the lowest percentage of sustained demurrers.)
Even though each judge has ruled fairly consistently in breach of contract cases, there are noticeable differences in how litigators have dinged particular judges with CCP 170.6 challenges.
Judge B, for example, received 23 percent of all CCP 170.6 challenges filed in breach of contract claims; 16 percent of these filings were initiated by plaintiffs. This appears to make sense. Judge B was, after all, the judge with the highest percentage of sustained demurrers. But what about Judge C? Judge C received only 6 percent of all CCP 170.6 challenges filed in breach of contract claims. However, all of these challenges were filed by plaintiffs. This, despite the fact that Judge C comes with the highest percentage of overruled (and the lowest percentage of sustained) demurrers in breach of contract claims. That is to say, Judge C appears to have been relatively plaintiff-friendly in breach of contract filings.
This raises the question: Why has a judge like Judge C received so many plaintiff-initiated challenges when based on the data, Judge C tends to favor Plaintiffs?
Trends in judicial rulings are not the only significant pieces of information to consider. It’s also important to understand how lawyers have acted on the peer-sourced information they have collected, both on judges and each other. Relying on anecdotes alone (as lawyers have historically done) may no longer serve litigators or their clients. It’s important to integrate hard data and legal analytics into strategic decision making as peer sourced information may be out of date, distorted, and inherently biased.