'Moneyball' for Litigators

For any given legal matter, a shrewd litigation manager should be interested in these three main data points:

1. How much is the matter worth (exposure level)?
2. Relative to its worth, what should the cost of representation be for the matter? (fee arrangement)?
3. What is the outcome?

Sophisticated litigation managers can use analytics to bring those three data points together. For example, they can determine whether the amount spent was appropriate relative to the matter's value and whether the expenditure resulted in the intended outcome. Analysis of the relationship among these data points will allow clients to reward outside counsel for results, not just for the number of hours put into the work, and will also provide useful information for selecting outside counsel and making fee arrangements for future engagements.

Oakland was able to use data to manage its spending, increase the baseball team's performance efficiency and position itself for wins. Similarly, litigation managers can use data analytics for spend management, performance evaluation, strategic case management and managing risk through assessing potential outcomes. While some level of data-based financial management is becoming more common, fewer litigators currently use data for strategic case analysis or risk management.

Legal Spend Analytics

Just as Oakland was able to manage its budgetary restrictions including salary caps, litigation managers can use data analytics to control, reduce or justify litigation spending. e-Billing data is a basic source for these analytics. It has typically been used for compliance purposes, i.e., whether counsel is billing according to the client's guidelines. Sophisticated users are now looking at their e-billing data as well as external sources to determine what that data means: what they should spend (budgeting), how costs can be reduced, and other factors regarding how to spend a limited budget wisely. Questions they can answer based on litigation spend analytics are the amount that should be spent on a given matter, the right law firm for the matter, and what specific activities for the matter should cost.

The degree of sophistication with which organizations manage litigation spend varies along a wide continuum. On the low end, organizations review hourly rates and ensure that billing is within a budget. The next levels of sophistication include discounted hourly rates, then task-based billing and budgeting. Alternative fee arrangements (AFAs), including fixed fees, are another level of sophistication. The most sophisticated levels of the financial management progression include value-based analysis: value-based budgeting, value-based fees and value-based fees taking into consideration total cost (outcome, value of case and cost of representation).

Most litigation managers fall somewhere between the lower and middle levels of this progression because they fail to usefully define the value of a case or of the representation. They may fail to do a valuation at all or make valuations that are not supported by data. But the biggest failure is the failure to define what counts as a “win.” Law firms and law departments that start with that premise ' “what is a good result” ' and work backwards from there, rather than starting with questions of staffing and hours, can more effectively develop AFAs and other value-based fees with a “win-win” foundation built.

Strategic Case Analytics

One of the key factors Beane looked at in building his team was players' ability to get to get on base. Like statistics that measure on-field performance, litigation data can be analyzed to measure counsel's performance and productivity as well as to determine case strategy.

Choosing the right team for the matter is the first step. Beyond that, litigation managers can use data analytics to assess and manage productivity. For example, how long should it take to file a motion, draft a memo, take a deposition? For that matter, how many motions, depositions, etc. are really needed? In essence, data that identifies productivity aspects of the law firm engagement can be used to facilitate project management.

Data can also be used to formulate strategies to “win” the case, with “win” being defined as achieving the desired outcome under the circumstances. It is intuitive to experienced litigators that the venue, the judge, counsel's track record, the parties' past history and the matter-specific facts all contribute to the likelihood of success. But few litigators actually quantify those factors. Mining “big” and “small” (your own) data on these factors, quantifying their relative weight, and analyzing the results can yield the basis for a decision tree analysis. This analysis cannot replace judgment, but it can position lawyers to make more informed decisions and can serve as a “reality check” for both clients who may have an emotional investment in a matter as well as counsel who may have overly confident opinions of their likelihood of success. This phenomena was examined in a study published by Goodman-Delahunty, J., et al. , titled “Insightful or Wishful: Lawyers' Ability to Predict Case Outcomes,” 16 Psychology, Public Policy, and Law 133 (2010). The authors compared nearly 500 lawyers' predictions of pending case outcomes against the actual results. The outcomes were worse than the predictions for over 40% of the lawyers interviewed, regardless of their level of experience. In fact, the authors noted that the most overconfident lawyers were senior partners who may not typically obtain other parties' feedback.

One of the pushbacks to using analytics for case strategy and predicting likely outcomes has been that analytics use computers to replace lawyers' judgment and knowledge base. Whether this is a real or perceived threat, the change toward embracing analytics for making strategic decisions will come when there is greater confidence in the supporting data, which can be used as a validation for moving forward.

Risk Reduction Analytics

On the most sophisticated level, data analytics can be used for risk reduction, not only with regard to current matters, but also in terms of diagnosing and remediating future exposure, reducing future spend and liability. Organizations can mine their business data to identify current exposure issues. For example, what areas of litigation are showing an upswing? Where are there an abundance of consumer inquiries? Which contract terms and conditions may offer exposure?

This type of analysis can actually change the value proposition for law departments and outside counsel. With traditional models, both inside and outside counsel are being paid primarily for work in progress, often of a defensive nature. But what if they were rewarded for reducing exposure? One major company has offered incentives for reducing total legal cost (fees, expenses, and resolution costs), case cycle times and administrative burdens, and also for reducing the number of matters at higher exposure levels. The company is able to measure these issues because of the data it has collected over time.

Conclusion

Litigators are only beginning to recognize the possibilities of “big data” and, to date, most have focused on its use strictly for financial management purposes. Most still have not tapped the potential for more sophisticated levels of spend management, nor have they explored strategic case analytics or risk reduction analytics. Those who do will become contenders for champions of their leagues.

Jim Michalowicz, a managing director at Huron Legal, is an expert in improving the operation and delivery of litigation management and e-discovery services within law departments and their outside counsel programs. Michalowicz implements cost control programs for law department and law firm clients using business analytics and performance metrics. He can be reached at [email protected].

Big data is ubiquitous these days, but still largely untapped in legal circles. Litigators can take a page out of a sports team's playbook and use the patterns and trends found in data to make more informed decisions about case staffing, spend management, case strategy and probable outcomes.

Michael Lewis' book Moneyball: The Art of Winning an Unfair Game describes how the Oakland Athletics and the team's general manager Billy Beane (played by Brad Pitt in the movie of the same name) relied on empirical data to assemble a competitive baseball team. Despite having one of the lowest payroll budgets in baseball, Beane was able to identify players who had success indicators such as on-base and slugging percentages, even though they were not necessarily the most desirable according to traditional standards. He used principles of “sabermetrics,” the empirical analysis of baseball statistics, to recruit a winning team.

Some people also think of litigation as a game, and in many ways it is similar to professional sports: Within their budgets, parties choose players to compete on their behalf; there are rules that must be followed and there is often discussion of whether the playing field is level; and there are certainly winners and losers. Like in baseball, litigators can use data analytics to choose the members on their teams based on money available to be invested, skill set needed, and situational factors (venue, opponent, winning percentage against opponent and in venue).

For any given legal matter, a shrewd litigation manager should be interested in these three main data points:

1. How much is the matter worth (exposure level)?
2. Relative to its worth, what should the cost of representation be for the matter? (fee arrangement)?
3. What is the outcome?

Sophisticated litigation managers can use analytics to bring those three data points together. For example, they can determine whether the amount spent was appropriate relative to the matter's value and whether the expenditure resulted in the intended outcome. Analysis of the relationship among these data points will allow clients to reward outside counsel for results, not just for the number of hours put into the work, and will also provide useful information for selecting outside counsel and making fee arrangements for future engagements.

Oakland was able to use data to manage its spending, increase the baseball team's performance efficiency and position itself for wins. Similarly, litigation managers can use data analytics for spend management, performance evaluation, strategic case management and managing risk through assessing potential outcomes. While some level of data-based financial management is becoming more common, fewer litigators currently use data for strategic case analysis or risk management.

Legal Spend Analytics

Just as Oakland was able to manage its budgetary restrictions including salary caps, litigation managers can use data analytics to control, reduce or justify litigation spending. e-Billing data is a basic source for these analytics. It has typically been used for compliance purposes, i.e., whether counsel is billing according to the client's guidelines. Sophisticated users are now looking at their e-billing data as well as external sources to determine what that data means: what they should spend (budgeting), how costs can be reduced, and other factors regarding how to spend a limited budget wisely. Questions they can answer based on litigation spend analytics are the amount that should be spent on a given matter, the right law firm for the matter, and what specific activities for the matter should cost.

The degree of sophistication with which organizations manage litigation spend varies along a wide continuum. On the low end, organizations review hourly rates and ensure that billing is within a budget. The next levels of sophistication include discounted hourly rates, then task-based billing and budgeting. Alternative fee arrangements (AFAs), including fixed fees, are another level of sophistication. The most sophisticated levels of the financial management progression include value-based analysis: value-based budgeting, value-based fees and value-based fees taking into consideration total cost (outcome, value of case and cost of representation).

Most litigation managers fall somewhere between the lower and middle levels of this progression because they fail to usefully define the value of a case or of the representation. They may fail to do a valuation at all or make valuations that are not supported by data. But the biggest failure is the failure to define what counts as a “win.” Law firms and law departments that start with that premise ' “what is a good result” ' and work backwards from there, rather than starting with questions of staffing and hours, can more effectively develop AFAs and other value-based fees with a “win-win” foundation built.

Strategic Case Analytics

One of the key factors Beane looked at in building his team was players' ability to get to get on base. Like statistics that measure on-field performance, litigation data can be analyzed to measure counsel's performance and productivity as well as to determine case strategy.

Choosing the right team for the matter is the first step. Beyond that, litigation managers can use data analytics to assess and manage productivity. For example, how long should it take to file a motion, draft a memo, take a deposition? For that matter, how many motions, depositions, etc. are really needed? In essence, data that identifies productivity aspects of the law firm engagement can be used to facilitate project management.

Data can also be used to formulate strategies to “win” the case, with “win” being defined as achieving the desired outcome under the circumstances. It is intuitive to experienced litigators that the venue, the judge, counsel's track record, the parties' past history and the matter-specific facts all contribute to the likelihood of success. But few litigators actually quantify those factors. Mining “big” and “small” (your own) data on these factors, quantifying their relative weight, and analyzing the results can yield the basis for a decision tree analysis. This analysis cannot replace judgment, but it can position lawyers to make more informed decisions and can serve as a “reality check” for both clients who may have an emotional investment in a matter as well as counsel who may have overly confident opinions of their likelihood of success. This phenomena was examined in a study published by Goodman-Delahunty, J., et al. , titled “Insightful or Wishful: Lawyers' Ability to Predict Case Outcomes,” 16 Psychology, Public Policy, and Law 133 (2010). The authors compared nearly 500 lawyers' predictions of pending case outcomes against the actual results. The outcomes were worse than the predictions for over 40% of the lawyers interviewed, regardless of their level of experience. In fact, the authors noted that the most overconfident lawyers were senior partners who may not typically obtain other parties' feedback.

One of the pushbacks to using analytics for case strategy and predicting likely outcomes has been that analytics use computers to replace lawyers' judgment and knowledge base. Whether this is a real or perceived threat, the change toward embracing analytics for making strategic decisions will come when there is greater confidence in the supporting data, which can be used as a validation for moving forward.

Risk Reduction Analytics

On the most sophisticated level, data analytics can be used for risk reduction, not only with regard to current matters, but also in terms of diagnosing and remediating future exposure, reducing future spend and liability. Organizations can mine their business data to identify current exposure issues. For example, what areas of litigation are showing an upswing? Where are there an abundance of consumer inquiries? Which contract terms and conditions may offer exposure?

This type of analysis can actually change the value proposition for law departments and outside counsel. With traditional models, both inside and outside counsel are being paid primarily for work in progress, often of a defensive nature. But what if they were rewarded for reducing exposure? One major company has offered incentives for reducing total legal cost (fees, expenses, and resolution costs), case cycle times and administrative burdens, and also for reducing the number of matters at higher exposure levels. The company is able to measure these issues because of the data it has collected over time.

Conclusion

Litigators are only beginning to recognize the possibilities of “big data” and, to date, most have focused on its use strictly for financial management purposes. Most still have not tapped the potential for more sophisticated levels of spend management, nor have they explored strategic case analytics or risk reduction analytics. Those who do will become contenders for champions of their leagues.

Jim Michalowicz, a managing director at Huron Legal, is an expert in improving the operation and delivery of litigation management and e-discovery services within law departments and their outside counsel programs. Michalowicz implements cost control programs for law department and law firm clients using business analytics and performance metrics. He can be reached at [email protected].