I was born in Altoga, Texas. It was just as rural as it sounds. Population 200, with more cows than people. I lived in a single-wide trailer on a rock road. What the house lacked in charm, my parents made up for with love. I remember my childhood as happy and full of warmth. My parents were at every game I ever played, ever teaching conference that was ever scheduled. I lived in the middle of nowhere. There were no kids around. My older brother and sister were 16 and 14 years older than me, so they had moved out. As a result, I spent lots of time alone, or with my mom and dad.
My mom and dad were my best friends who kicked the soccer ball with me, played video games, and watched TV with me. My dad was a road construction worker. He poured asphalt in the Texas heat in blue jeans in boots. Asphalt goes down at about 300 degrees, and in the summer, most days it is over 100. It was hard, dirty work. My dad never complained. When I smell asphalt being poured, I always smile, and I think of my dad. He died a few years ago. Mom was a hard worker too. She got up at 3:30am to leave at 4:00 am to get to work at 4:30 a.m. She worked as a waitress at a greasy spoon serving locals and truckers. She worked 9-10 hours per day, six days per week. Then she came home, cooked, cleaned, and did almost everything around the house. She didn’t complain either.
I loved sports. I played soccer, baseball, basketball, football, and track. I was a very good inside linebacker and I loved the intensity and contact. School was easy for me, and I liked it. I got an academic scholarship to go to a Lutheran college called Concordia University. I went with the plan to be a teacher. I met Alicia there. She was going to be a teacher too. I ended up working as a high school teacher for five years, including a year in the inner city, teaching a variety of kids including a fair number of gang members. After two years of teaching, I knew it wasn’t going to be something I did for 30 years. Alicia felt the same way. So we got married and decided to go to law school. Since we had no money and we were sort of clueless – we packed up, moved to St. Louis (it was cheap and we knew a few people there). We taught high school during the day, raced from work to class, and went to law school at night. We graduated in 3.5 years, sleep-deprived, but proud.
I worked for John Simon’s law firm, and there I learned how to try cases. I also argued a lot of appeals – something that people typically don’t know about me. I’ve handled appeals in four federal circuits, three state courts, and I was the “Most Influential Appellate Lawyer” in Missouri twice. In my time in St. Louis gained a reputation as a sometimes too aggressive, reasonably clever, and trustworthy lawyer. I helped on injury cases but my primary job was running a class action department. It did well, and I liked the variety of cases and the challenge.
Wyatt Che, our first son, was born, and I wasn’t going to have a son I didn’t see. So, I applied for a job as a law professor at the University of Denver College of Law, somehow got it, and we moved to Denver. I kept a book of about 20 class cases. Alicia kept a full case load in St. Louis and called the Southwest flight from Denver to St. Louis her commute. People don’t know this about Alicia, but during those first years in Denver, she tried and settled more mortgage fraud cases than perhaps any attorney in the country. She was fearless and gained a reputation as such. I once saw her, semi-lovingly, shove a male opposing counsel in court when he offered her too little money. He laughed, she kept pushing physically and metaphorically, and he paid a boat load of money with the jury in the hallway, waiting. Alicia loved the work because she loves people. She is the only lawyer I know who baked cookies in her office so her homeless clients (she did social security disability work too) could come in from the cold and have a snack. We moved to Denver for the new job seven days after John Atticus (Jack) was born. He and Wyatt are 15 months apart. At the University of Denver Law School, I taught evidence, torts, an appellate class, and legal research and writing. I also learned about online studies, big data, and statistical methods. Blah. Blah. Blah. Published some academic papers. Started doing big data. Had some luck with that, and still love it.
A yearlong sabbatical from the University of Denver led us to Madrid. It was supposed to be a single year, but we loved it and so did our kids. We adjusted our lives, moved there, bought a place, gutted and reformed it, and live there now during the school year. We all speak Spanish now and have been in Madrid for three years. Wyatt is 11. He is sweet and creative, a water polo player, and our first born. He loves Minecraft, he is especially soft-hearted about animals, and he loves new things. Jack is 10. He is sweet too, but also unusually intense and competitive. He always has been ever since slide tackling a kid in soccer at three years old. He is a natural athlete, has gotten into inventing movie trailers for his imaginary films (mostly horror films for some reason) and still likes to cuddle. The four of us are a team, a pretty decent golf foursome, and we like it like that. If we aren’t in Madrid, traveling as a family, or traveling for work, you’ll find us on the Big Island. It is the place I feel most at home and happy on earth. We just bought some land there and just knowing someday I can live there at least part of the year makes me very happy and hopeful.
Random other stuff that doesn’t fit anywhere: I love golf. I am attracted to almost anything new to learn about. I learned how to hypnotize people, and I think I remember how. I worked every job imaginable, starting at 14. Bus boy. Newspaper delivery. Stocker in hospital supply room. Waiter. Usher at a movie theater. Construction site worker. I like to build things sometimes, and I built our boys a clubhouse in Colorado complete with a shingled roof, insulation, two levels, and electricity. I call Alicia before almost every argument, of any size in any court, because I’ve never stopped getting nervous and she makes me feel calm. I love craft beer. I was an evangelical Christian until my twenties. I’m an agnostic now. I work best to Pearl Jam. I think Appetite for Destruction (GNR) and August and Everything After (Counting Crows) are near perfect albums. I could eat poke or Mexican food just about every day.
John and Sean will spend the first hour discussing “What is Big Data and How Can Lawyers Use It”. While the law has traditionally been considered an art, with intuition and instinct playing a major role in decision-making, there should be a larger emphasis on data and science in the field. Many questions in the law have fixed and correct answers, and lawyers should not be guessing at these answers but instead using data to inform their decisions. The first hour of this presentation provides examples of questions that have concrete answers, such as whether to begin an opening statement by discussing the plaintiff's injury or the defendant's conduct. John and Sean will show that bringing more science to cases does not eliminate the art of lawyering but elevates it. To illustrate this point, we will discusses the development of a computer program, AlphaGo, which learned to play the board game Go through deep learning and eventually beat the world champion. Just as AlphaGo taught the champion new tricks, bringing more science to law can empower great lawyers to try even better cases.
People are predictable, even when their behavior is irrational. Human cognitive fallacies, which are predictable, repeatable, and chronicled in books like; Thinking, Fast and Slow and Predictably Irrational, can be used to lawyers advantage if they ask the right empirical questions and study them correctly. During this hour we will stress the importance of testing empirical questions to avoid self-inflicted, expensive wounds, and emphasizes that trying a case without obtaining the answers to empirical questions is equivalent to guessing. Finally, we will explain that the true value of a case, within a range, is knowable and should be determined through data, not instincts, to avoid donating money to the defense. We will use numerous real life examples where we have conducted big data studies and have jury verdicts to back up and support the data.
We will address the prevalence of old wives' tales or myths in the legal profession. These are often well-intentioned but lack empirical evidence to support them. One example is the belief that certain demographics of jurors are better or worse for a case, such as liberal vs. conservative or black vs. white jurors. However, we will show the effectiveness of jurors depends entirely on the case and can be determined through empirical analysis. We will also challenge other myths, such as not submitting low economic damages, highlighting bad facts in mini-openings, and starting with the defendant's bad conduct. We will encourage attendees to be open-minded and challenge these myths, as they may not hold true in all cases.
Every case is unique, and there is no one-size-fits-all approach to handling cases. Even small changes in a case can lead to significant differences in outcomes. Therefore, it is important to approach each case with an open mind and make decisions based on data and evidence rather than relying on predetermined strategies or approaches. We will show an example of how omitting an economic damages request in one case led to increased damages and win rate, but in another case, omitting the economic damages request could have decreased damages and win rate. The key takeaway is that there is no Rosetta Stone for handling cases, and every case requires a unique and data-driven approach. We will compare a legal case to a product that needs to be refined and tested before it is presented to the market, which in this case is the jury. We will emphasize the importance of conducting market research to determine the viability of a case and to identify the target audience. Testing the case with real people, gathering data, and refining it accordingly is crucial to ensure success in the courtroom. We will stress the need to move away from guessing and instinctual decision-making and toward evidence-based methods for optimizing a case.
We will highlight the importance and usefulness of the knowledge and strategies shared by the greats of trial law. Data is not meant to replace these strategies, but to fuel them. Examples of the greats include Rick Friedman, who emphasizes identifying simple rules that jurors understand and agree to, Mark Mandell, who focuses on finding the best and worst issues in a case, and Nick and Courtney Rowley, who teach about being brutally honest and credible with the jury. We will explain how data can help enhance these strategies, such as testing different lost wage claims to determine what jurors believe to be fair and being able to accurately rate the severity of an injury based on jurors' intentions for award size. The more precise and powerful data an attorney has, the better they can prepare for trial.
To be a successful trial lawyer, you need to reduce the number of cases you work on. With too many cases, there is not enough time to properly prepare for depositions, review documents, draft motions, and conduct trials. We will discuss singularly focusing on one case during workdays, which are typically two days that last 12-16 hours per day without distractions. The firm requires the referring attorney to provide them with all relevant documents and answer important worksheets focusing on the order of proof, timeline, and defendant's excuses. We will discuss how to use big data to understand what drives good and bad jurors to deliver a verdict and to prepare voir dire questions and opening statements. We will emphasizes the need to be objective and consider every potential defense that the defendant might raise. The goal is to turn a mountain of information into a laser-focused set of documents, witnesses, and facts that will drive the largest verdict possible.
John and Sean will spend the first hour discussing “What is Big Data and How Can Lawyers Use It”. While the law has traditionally been considered an art, with intuition and instinct playing a major role in decision-making, there should be a larger emphasis on data and science in the field. Many questions in the law have fixed and correct answers, and lawyers should not be guessing at these answers but instead using data to inform their decisions. The first hour of this presentation provides examples of questions that have concrete answers, such as whether to begin an opening statement by discussing the plaintiff's injury or the defendant's conduct. John and Sean will show that bringing more science to cases does not eliminate the art of lawyering but elevates it. To illustrate this point, we will discusses the development of a computer program, AlphaGo, which learned to play the board game Go through deep learning and eventually beat the world champion. Just as AlphaGo taught the champion new tricks, bringing more science to law can empower great lawyers to try even better cases.
People are predictable, even when their behavior is irrational. Human cognitive fallacies, which are predictable, repeatable, and chronicled in books like; Thinking, Fast and Slow and Predictably Irrational, can be used to lawyers advantage if they ask the right empirical questions and study them correctly. During this hour we will stress the importance of testing empirical questions to avoid self-inflicted, expensive wounds, and emphasizes that trying a case without obtaining the answers to empirical questions is equivalent to guessing. Finally, we will explain that the true value of a case, within a range, is knowable and should be determined through data, not instincts, to avoid donating money to the defense. We will use numerous real life examples where we have conducted big data studies and have jury verdicts to back up and support the data.
We will address the prevalence of old wives' tales or myths in the legal profession. These are often well-intentioned but lack empirical evidence to support them. One example is the belief that certain demographics of jurors are better or worse for a case, such as liberal vs. conservative or black vs. white jurors. However, we will show the effectiveness of jurors depends entirely on the case and can be determined through empirical analysis. We will also challenge other myths, such as not submitting low economic damages, highlighting bad facts in mini-openings, and starting with the defendant's bad conduct. We will encourage attendees to be open-minded and challenge these myths, as they may not hold true in all cases.
Every case is unique, and there is no one-size-fits-all approach to handling cases. Even small changes in a case can lead to significant differences in outcomes. Therefore, it is important to approach each case with an open mind and make decisions based on data and evidence rather than relying on predetermined strategies or approaches. We will show an example of how omitting an economic damages request in one case led to increased damages and win rate, but in another case, omitting the economic damages request could have decreased damages and win rate. The key takeaway is that there is no Rosetta Stone for handling cases, and every case requires a unique and data-driven approach. We will compare a legal case to a product that needs to be refined and tested before it is presented to the market, which in this case is the jury. We will emphasize the importance of conducting market research to determine the viability of a case and to identify the target audience. Testing the case with real people, gathering data, and refining it accordingly is crucial to ensure success in the courtroom. We will stress the need to move away from guessing and instinctual decision-making and toward evidence-based methods for optimizing a case.
We will highlight the importance and usefulness of the knowledge and strategies shared by the greats of trial law. Data is not meant to replace these strategies, but to fuel them. Examples of the greats include Rick Friedman, who emphasizes identifying simple rules that jurors understand and agree to, Mark Mandell, who focuses on finding the best and worst issues in a case, and Nick and Courtney Rowley, who teach about being brutally honest and credible with the jury. We will explain how data can help enhance these strategies, such as testing different lost wage claims to determine what jurors believe to be fair and being able to accurately rate the severity of an injury based on jurors' intentions for award size. The more precise and powerful data an attorney has, the better they can prepare for trial.
To be a successful trial lawyer, you need to reduce the number of cases you work on. With too many cases, there is not enough time to properly prepare for depositions, review documents, draft motions, and conduct trials. We will discuss singularly focusing on one case during workdays, which are typically two days that last 12-16 hours per day without distractions. The firm requires the referring attorney to provide them with all relevant documents and answer important worksheets focusing on the order of proof, timeline, and defendant's excuses. We will discuss how to use big data to understand what drives good and bad jurors to deliver a verdict and to prepare voir dire questions and opening statements. We will emphasizes the need to be objective and consider every potential defense that the defendant might raise. The goal is to turn a mountain of information into a laser-focused set of documents, witnesses, and facts that will drive the largest verdict possible.
John and Sean will spend the first hour discussing “What is Big Data and How Can Lawyers Use It”. While the law has traditionally been considered an art, with intuition and instinct playing a major role in decision-making, there should be a larger emphasis on data and science in the field. Many questions in the law have fixed and correct answers, and lawyers should not be guessing at these answers but instead using data to inform their decisions. The first hour of this presentation provides examples of questions that have concrete answers, such as whether to begin an opening statement by discussing the plaintiff's injury or the defendant's conduct. John and Sean will show that bringing more science to cases does not eliminate the art of lawyering but elevates it. To illustrate this point, we will discusses the development of a computer program, AlphaGo, which learned to play the board game Go through deep learning and eventually beat the world champion. Just as AlphaGo taught the champion new tricks, bringing more science to law can empower great lawyers to try even better cases.
People are predictable, even when their behavior is irrational. Human cognitive fallacies, which are predictable, repeatable, and chronicled in books like; Thinking, Fast and Slow and Predictably Irrational, can be used to lawyers advantage if they ask the right empirical questions and study them correctly. During this hour we will stress the importance of testing empirical questions to avoid self-inflicted, expensive wounds, and emphasizes that trying a case without obtaining the answers to empirical questions is equivalent to guessing. Finally, we will explain that the true value of a case, within a range, is knowable and should be determined through data, not instincts, to avoid donating money to the defense. We will use numerous real life examples where we have conducted big data studies and have jury verdicts to back up and support the data.
We will address the prevalence of old wives' tales or myths in the legal profession. These are often well-intentioned but lack empirical evidence to support them. One example is the belief that certain demographics of jurors are better or worse for a case, such as liberal vs. conservative or black vs. white jurors. However, we will show the effectiveness of jurors depends entirely on the case and can be determined through empirical analysis. We will also challenge other myths, such as not submitting low economic damages, highlighting bad facts in mini-openings, and starting with the defendant's bad conduct. We will encourage attendees to be open-minded and challenge these myths, as they may not hold true in all cases.
Every case is unique, and there is no one-size-fits-all approach to handling cases. Even small changes in a case can lead to significant differences in outcomes. Therefore, it is important to approach each case with an open mind and make decisions based on data and evidence rather than relying on predetermined strategies or approaches. We will show an example of how omitting an economic damages request in one case led to increased damages and win rate, but in another case, omitting the economic damages request could have decreased damages and win rate. The key takeaway is that there is no Rosetta Stone for handling cases, and every case requires a unique and data-driven approach. We will compare a legal case to a product that needs to be refined and tested before it is presented to the market, which in this case is the jury. We will emphasize the importance of conducting market research to determine the viability of a case and to identify the target audience. Testing the case with real people, gathering data, and refining it accordingly is crucial to ensure success in the courtroom. We will stress the need to move away from guessing and instinctual decision-making and toward evidence-based methods for optimizing a case.
We will highlight the importance and usefulness of the knowledge and strategies shared by the greats of trial law. Data is not meant to replace these strategies, but to fuel them. Examples of the greats include Rick Friedman, who emphasizes identifying simple rules that jurors understand and agree to, Mark Mandell, who focuses on finding the best and worst issues in a case, and Nick and Courtney Rowley, who teach about being brutally honest and credible with the jury. We will explain how data can help enhance these strategies, such as testing different lost wage claims to determine what jurors believe to be fair and being able to accurately rate the severity of an injury based on jurors' intentions for award size. The more precise and powerful data an attorney has, the better they can prepare for trial.
To be a successful trial lawyer, you need to reduce the number of cases you work on. With too many cases, there is not enough time to properly prepare for depositions, review documents, draft motions, and conduct trials. We will discuss singularly focusing on one case during workdays, which are typically two days that last 12-16 hours per day without distractions. The firm requires the referring attorney to provide them with all relevant documents and answer important worksheets focusing on the order of proof, timeline, and defendant's excuses. We will discuss how to use big data to understand what drives good and bad jurors to deliver a verdict and to prepare voir dire questions and opening statements. We will emphasizes the need to be objective and consider every potential defense that the defendant might raise. The goal is to turn a mountain of information into a laser-focused set of documents, witnesses, and facts that will drive the largest verdict possible.
John and Sean will spend the first hour discussing “What is Big Data and How Can Lawyers Use It”. While the law has traditionally been considered an art, with intuition and instinct playing a major role in decision-making, there should be a larger emphasis on data and science in the field. Many questions in the law have fixed and correct answers, and lawyers should not be guessing at these answers but instead using data to inform their decisions. The first hour of this presentation provides examples of questions that have concrete answers, such as whether to begin an opening statement by discussing the plaintiff's injury or the defendant's conduct. John and Sean will show that bringing more science to cases does not eliminate the art of lawyering but elevates it. To illustrate this point, we will discusses the development of a computer program, AlphaGo, which learned to play the board game Go through deep learning and eventually beat the world champion. Just as AlphaGo taught the champion new tricks, bringing more science to law can empower great lawyers to try even better cases.
People are predictable, even when their behavior is irrational. Human cognitive fallacies, which are predictable, repeatable, and chronicled in books like; Thinking, Fast and Slow and Predictably Irrational, can be used to lawyers advantage if they ask the right empirical questions and study them correctly. During this hour we will stress the importance of testing empirical questions to avoid self-inflicted, expensive wounds, and emphasizes that trying a case without obtaining the answers to empirical questions is equivalent to guessing. Finally, we will explain that the true value of a case, within a range, is knowable and should be determined through data, not instincts, to avoid donating money to the defense. We will use numerous real life examples where we have conducted big data studies and have jury verdicts to back up and support the data.
We will address the prevalence of old wives' tales or myths in the legal profession. These are often well-intentioned but lack empirical evidence to support them. One example is the belief that certain demographics of jurors are better or worse for a case, such as liberal vs. conservative or black vs. white jurors. However, we will show the effectiveness of jurors depends entirely on the case and can be determined through empirical analysis. We will also challenge other myths, such as not submitting low economic damages, highlighting bad facts in mini-openings, and starting with the defendant's bad conduct. We will encourage attendees to be open-minded and challenge these myths, as they may not hold true in all cases.
Every case is unique, and there is no one-size-fits-all approach to handling cases. Even small changes in a case can lead to significant differences in outcomes. Therefore, it is important to approach each case with an open mind and make decisions based on data and evidence rather than relying on predetermined strategies or approaches. We will show an example of how omitting an economic damages request in one case led to increased damages and win rate, but in another case, omitting the economic damages request could have decreased damages and win rate. The key takeaway is that there is no Rosetta Stone for handling cases, and every case requires a unique and data-driven approach. We will compare a legal case to a product that needs to be refined and tested before it is presented to the market, which in this case is the jury. We will emphasize the importance of conducting market research to determine the viability of a case and to identify the target audience. Testing the case with real people, gathering data, and refining it accordingly is crucial to ensure success in the courtroom. We will stress the need to move away from guessing and instinctual decision-making and toward evidence-based methods for optimizing a case.
We will highlight the importance and usefulness of the knowledge and strategies shared by the greats of trial law. Data is not meant to replace these strategies, but to fuel them. Examples of the greats include Rick Friedman, who emphasizes identifying simple rules that jurors understand and agree to, Mark Mandell, who focuses on finding the best and worst issues in a case, and Nick and Courtney Rowley, who teach about being brutally honest and credible with the jury. We will explain how data can help enhance these strategies, such as testing different lost wage claims to determine what jurors believe to be fair and being able to accurately rate the severity of an injury based on jurors' intentions for award size. The more precise and powerful data an attorney has, the better they can prepare for trial.
To be a successful trial lawyer, you need to reduce the number of cases you work on. With too many cases, there is not enough time to properly prepare for depositions, review documents, draft motions, and conduct trials. We will discuss singularly focusing on one case during workdays, which are typically two days that last 12-16 hours per day without distractions. The firm requires the referring attorney to provide them with all relevant documents and answer important worksheets focusing on the order of proof, timeline, and defendant's excuses. We will discuss how to use big data to understand what drives good and bad jurors to deliver a verdict and to prepare voir dire questions and opening statements. We will emphasizes the need to be objective and consider every potential defense that the defendant might raise. The goal is to turn a mountain of information into a laser-focused set of documents, witnesses, and facts that will drive the largest verdict possible.
John and Sean will spend the first hour discussing “What is Big Data and How Can Lawyers Use It”. While the law has traditionally been considered an art, with intuition and instinct playing a major role in decision-making, there should be a larger emphasis on data and science in the field. Many questions in the law have fixed and correct answers, and lawyers should not be guessing at these answers but instead using data to inform their decisions. The first hour of this presentation provides examples of questions that have concrete answers, such as whether to begin an opening statement by discussing the plaintiff's injury or the defendant's conduct. John and Sean will show that bringing more science to cases does not eliminate the art of lawyering but elevates it. To illustrate this point, we will discusses the development of a computer program, AlphaGo, which learned to play the board game Go through deep learning and eventually beat the world champion. Just as AlphaGo taught the champion new tricks, bringing more science to law can empower great lawyers to try even better cases.
People are predictable, even when their behavior is irrational. Human cognitive fallacies, which are predictable, repeatable, and chronicled in books like; Thinking, Fast and Slow and Predictably Irrational, can be used to lawyers advantage if they ask the right empirical questions and study them correctly. During this hour we will stress the importance of testing empirical questions to avoid self-inflicted, expensive wounds, and emphasizes that trying a case without obtaining the answers to empirical questions is equivalent to guessing. Finally, we will explain that the true value of a case, within a range, is knowable and should be determined through data, not instincts, to avoid donating money to the defense. We will use numerous real life examples where we have conducted big data studies and have jury verdicts to back up and support the data.
We will address the prevalence of old wives' tales or myths in the legal profession. These are often well-intentioned but lack empirical evidence to support them. One example is the belief that certain demographics of jurors are better or worse for a case, such as liberal vs. conservative or black vs. white jurors. However, we will show the effectiveness of jurors depends entirely on the case and can be determined through empirical analysis. We will also challenge other myths, such as not submitting low economic damages, highlighting bad facts in mini-openings, and starting with the defendant's bad conduct. We will encourage attendees to be open-minded and challenge these myths, as they may not hold true in all cases.
Every case is unique, and there is no one-size-fits-all approach to handling cases. Even small changes in a case can lead to significant differences in outcomes. Therefore, it is important to approach each case with an open mind and make decisions based on data and evidence rather than relying on predetermined strategies or approaches. We will show an example of how omitting an economic damages request in one case led to increased damages and win rate, but in another case, omitting the economic damages request could have decreased damages and win rate. The key takeaway is that there is no Rosetta Stone for handling cases, and every case requires a unique and data-driven approach. We will compare a legal case to a product that needs to be refined and tested before it is presented to the market, which in this case is the jury. We will emphasize the importance of conducting market research to determine the viability of a case and to identify the target audience. Testing the case with real people, gathering data, and refining it accordingly is crucial to ensure success in the courtroom. We will stress the need to move away from guessing and instinctual decision-making and toward evidence-based methods for optimizing a case.
We will highlight the importance and usefulness of the knowledge and strategies shared by the greats of trial law. Data is not meant to replace these strategies, but to fuel them. Examples of the greats include Rick Friedman, who emphasizes identifying simple rules that jurors understand and agree to, Mark Mandell, who focuses on finding the best and worst issues in a case, and Nick and Courtney Rowley, who teach about being brutally honest and credible with the jury. We will explain how data can help enhance these strategies, such as testing different lost wage claims to determine what jurors believe to be fair and being able to accurately rate the severity of an injury based on jurors' intentions for award size. The more precise and powerful data an attorney has, the better they can prepare for trial.
To be a successful trial lawyer, you need to reduce the number of cases you work on. With too many cases, there is not enough time to properly prepare for depositions, review documents, draft motions, and conduct trials. We will discuss singularly focusing on one case during workdays, which are typically two days that last 12-16 hours per day without distractions. The firm requires the referring attorney to provide them with all relevant documents and answer important worksheets focusing on the order of proof, timeline, and defendant's excuses. We will discuss how to use big data to understand what drives good and bad jurors to deliver a verdict and to prepare voir dire questions and opening statements. We will emphasizes the need to be objective and consider every potential defense that the defendant might raise. The goal is to turn a mountain of information into a laser-focused set of documents, witnesses, and facts that will drive the largest verdict possible.
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