Last night, the Los Angeles Dodgers defeated the Tampa Bay Rays to win the 2020 World Series. It was the Dodgers' first title in 32 years. A fateful decision changed the dynamics of the game.
For the first six innings, Tampa Bay had a 1-0 lead. This was because her starter mug, the Cy Young Award winning Blake Snell, was doing very well. He hit nine and only gave up two hits. His performance has been compared to the Hall of Famer Sandy Koufax.
But at the sixth inning, shortly after Snell gave up a baseline goal, Rays manager Kevin Cash replaced him with Nick Anderson. That decision failed when Anderson gave up two runs that inning, including one due to a wild pitch. Unfortunately for Tampa Bay, they couldn't recover from that.
I don't follow baseball so I was confused by Cash's decision. I didn't think a pitcher would be replaced after just two hits. I thought substitutions happened when a player lost his lead and gave up numerous hits and runs.
I was told that the manager probably replaced Snell because he relied on analytics. There is a huge amount of data on baseball stats such as: B. Games played, results and player attributes. All of this information is analyzed and synthesized to provide advice to teams on who is likely to win a particular game and when a pitcher should be exonerated. There's even a term for it: sabremetrics. I suppose it's not much different than two computer AIs playing against each other in a baseball video game.
The problem is, analytics is not as easy as that, as Tampa Bay painfully learned last night. If each team uses analytics to plan their season, only one team will benefit while the rest will either need other players or a more powerful AI that can better synthesize the data.
I was wondering how much the legal profession relies on their own analysis to make decisions.
We rely on precedents to predict how a similar case will be resolved. We analyze a judge's previous decisions to prepare pleadings and oral arguments that will convince the judge to rule in our favor. We review a juror based on race, education, social and economic standing, and a host of other factors to determine if they are personable to our client. The reputation and performance of the opposing attorney's attorney could also play a role in the course of action.
As AI and machine learning technologies develop, the analysis becomes more detailed. They will delve into court rulings, law review articles, speeches from judges, Yelp attorney reviews, and other information they can find on the web.
Another problem is access. The richer law firms are likely to be the first to adopt this technology and use it to the benefit of their clients. While smaller businesses with more humble or needy customers have to wait for the technology to reach its price point. This injustice is nothing new. The rich can buy more resources because they have the money.
So let's say a lawyer has a strong argument and believes that he is likely to win in court. But what if the "analytics" say that the judge is likely to rule against his applications? Or does the AI program believe that the jury will likely rule against its client? Of course nobody can tell. Just like no one can say if Tampa Bay would have won last night if Blake Snell had stayed in the game.
If you rely on analytics, you may not get the result you want. Whether it's litigation or transactional work, there are many moving parts involved. Some parts we can predict with great accuracy, while others are a crapshoot. Judges and jurors are human and can change. Lawyers can lose their edge or get better over time. Economies and laws can change.
In the future, people and professionals will rely more on analytics data to make their decisions. But analytics is an educated guess at best. In a changing and unpredictable world, it may still be better to rely on your intuition, especially if it has served you well in the past.
Steven Chung is a tax attorney based in Los Angeles, California. He helps people with basic tax planning and tax dispute resolution. He is also personable with people with large student loans. He can be reached by email at [email protected] Or you can connect with him on Twitter (@stevenchung) and connect with him LinkedIn.