Cases are made up of events that can be counted in additional detail. And those individual events have characteristics that can also be quantified. In this article I will discuss these concepts with an apology up front that I will be using a lot of the game of Baseball analogies with apologies to my friends who follow the game of Cricket. That said…
In the simplest view, both Baseball and Cricket are games of discrete events where the pitcher (bowler) throws the ball and the batter (batsman) attempts to hit it. These games are subject to intense collection of statistical information because of the millions of dollars that can be spent on a specific player. But also, in recent years the data collection and analysis have been revolutionized by the use of computer technology. The type of throw, position, and success or failure against specific batters/batsmen is captured and analyzed. In recent years the statistics are combined into various comparative measures such as OBP that stands for “On-Base Percentage”. This metric revolutionized player value measurement because it recognized a key goal of the game is to get on base so as to be able to score a run that determines the final score; no matter whether they get there with the less “manly” walk or with a hit that was measured in “Batting Average”. The movie and book named Moneyball (see footnote 1) explains this revolution in Baseball statistical analysis and how it changed the way players are valued and game strategy in more entertaining form.
Continuing with this general theme (see footnote 2), courts also capture event information that is turned into statistical reports. In general, the events of the case filed and case end (at least temporarily) are ones that courts everywhere in the world report. But it is rare to see much more detail in court reports with the exception of trial and hearing event counts.
I have argued since 1997 that overall event counts should be treated with equal attention as case counts. After all case complexity and hence time spent on the processes and decisions can vary widely. In that I propose that court should add the following in their statistical reporting that I would also argue can be automatically calculated by a good automated Court Case Management System.
1. Total events recorded by the court during a time period. This reflects both work done by judges but also by the registry/clerk’s office by counting every event including documents received and sent by the registry/clerks/judges. This statistic can capture trends over time that may affect not only judicial resources but also support and ancillary resources.
2. Events per case by case type during a time period. This metric differentiates the amount of work performed by the various case types. It would in turn reflect the complexity or simplicity of the case types. And I would guess that it would also promote the further differentiation of case types in order to better understand the courts caseload.
3. Average time per event. There is quite a bit of concern that this is a difficult statistic to create. I would argue that computer technology makes this much easier to form. For example, documents in their “metadata” (data about data) automatically records the amount of time that it has taken to create it. So a computer generated document from the case management system would literally only take seconds to create.
Another way to capture time per event is in courtroom proceedings. If the court case management system is used in the courtroom to retrieve information from the calendar, or case details the date and time of that retrieval can be captured. Then when the next case is retrieved the “end” time for the first case is recorded and the second case is started. But this is not as good as where there are digital audio recording systems that capture the beginning and ending dates and time as part of their operating procedures. This information can be combined with the case information via the case number for categorization.
4. Events per Judicial, Staff, and Total Resources. I believe that the title of this section is self-explanatory. The idea comes from experience I have had with similar courts needing many more judges and staff compared to another division in another part of the state or country to achieve the same performance. So the statistic counts should likely be divided by case type. In doing this, the statistic has the potential to be an indication of the efficiency of a particular judicial institution division in comparison with another. But it can also be an indication of local procedural differences and implementation that in turn may connect with identifying audit or training needs.
5. Caseflow Plan Variance. Specific events, particularly those defined by the courts caseflow management (see footnote 3) plan should occur based on a set schedule. This metric assumes that the caseflow plan will be loaded into the CCMS as “planned events”. And when those “planned events” are cleared by the performance of those events by the parties or the court. The success or failure of the caseflow plan approach can be reflected in the adherence or variance from the plan. I would guess that this should be measured in days of difference from the planned and actual event occurrence. The highest measure of success would be essentially a zero (0) variance rate. Thus a case type, caseflow plan that would have a high variance number should be examined as to either the effectiveness of the plan or, in the administration of the plan by a court or even judicial official. The court case management system has the event information and if designed correctly, the plan should also be able to be loaded into the event system as well.
These are but a few ideas for expanding our use and understanding of the court’s work by using the data we have recorded in our CCMS.
If one is interested, I can explain how this can be done depending on the design of your CCMS if you wish to contact me. But also understand that there is additional work to do in judicial performance and particularly effectiveness as it relates to societal goals.
Last, please share below in the comments any ideas that you may have and, also any Cricket statistical references as well. I look forward to our conversation on this and related subjects.
(1) Moneyball book - https://en.wikipedia.org/wiki/Moneyball & https://en.wikipedia.org/wiki/Moneyball_(film)
(2) I must also once again give credit to my good friend, Ms. Janet Cornell and her groundbreaking work a court in Arizona in expanding court measures that was documented in her article in Court Manager at: http://www.ncsc.org/sitecore/content/microsites/future-trends-2013/home/Monthly-Trends-Articles/Evidence-Based-Management-for-Tomorrows-Successful-Court-Leader.aspx