Brad Pitt is sitting in a movie studio talking to Jonah Hill about a self-made baseball team changing the game and winning 20 straight to surpass the all-time record. The movie – “Moneyball” – went on to be nominated for six Academy Awards including Best Picture. It is a fictional and entertaining drama. Except it isn’t.
Before Pitt and Hill represented the Oakland A’s in the movie, “Moneyball” took place in real life and its story is considered to be very close to the true story (retold in the book “Moneyball” by Michael Lewis).
In 2002, the Oakland Athletics were dwarfed by industry giants such as the New York Yankees and San Francisco Giants. The odds were stacked against the franchise, but the A’s were still determined to win. In order to do this, they need to be prepared and consider the opposition to the meeting.
What does this have to do with insurance? Every company starts somewhere with data.
The power of data
The pivot to success was easy for the A’s when they went to support something that every decision-making team has in front of them – data. Baseball has made and destroyed data for over 100 years. We can say with certainty that we know what happened between the Cleveland Spiders and the Brooklyn Superbas of the National League in 1899 (the spiders lost – they have the worst record in Major League history).
What General Manager Billy Beane and Assistant General Manager Paul Depodesta (“Peter Brand” in the video) discovered was the real power in the data when it came to the element of the game – scoring runs more than the opponent. Most of his beliefs fit with conventional wisdom (home runs are good), but some did not and it was on the margins that, through the draft, Oakland won.
Twenty years later, we have entered the era of Moneyball insurance.
Like baseball, insurance is also very efficient and uses data, which has some knowledge of more than a century. It has titans based on history and ideas based on things that are about to disrupt. It has “losses”.
The time is now for insurance data reform
There are several reasons why it takes 20 years for data to enter insurance. In sports, new things are wild and impactful. Insurance is big and it is considered to be less than sports. Winning and losing are not clearly defined. Technological progress has lagged behind – especially with advanced data collection tools like telematics (similar to SportVue or Hit and Pitch f/x in baseball).
But now we are at the forefront of data, technology and business insurance.
Three types of commercial insurance are established during Moneyball
The ‘number one’ principle
In the film, Hill’s expert said, “It’s about reducing things to one number. By using the numbers the way we calculate them, we’re going to find players that no one else can see.
In insurance, the cost is the risk. Two major advances are currently improving the ability of companies to do this.
First, non-standard data exists and is available (mostly in the public domain) to compare almost any business model with its risk profile. Instead of using historical data, a comprehensive picture of the business can be used to understand it.
Second, thinking about capital has redefined what risk means. In SaaS (software as a service) markets where VC investment has thrived for years, the cost of living dominates. This concept refers to the insurance considering the purchase price, the cost of work, the opportunity to improve, the experience of customers, the possibility of abandonment, the possibility of collection and many other things that may not always be losses or combined costs. This is similar to focusing on the bases rather than hitting up the middle like a run in baseball (the main theme of Moneyball).
Understanding risk and the full meaning of what it means and a picture of what the business does can lead to the same benefits that Oakland uncovered. This brings the whole universe together – from the policy holders, shareholders, carriers, to the beneficiaries.
Every culture is important
For the past 20 years or so, online sportsbooks, brick and mortar casinos and even live sports arenas have offered betting lines on sports. This is a bet on who will win the game. In Europe and Asia, sports betting makes up more than 70% of all sports betting.
For about 15 out of 20 years, sports betting lines were more profitable because of how they were calculated.
To determine the probability that each team will win, a prediction algorithm can look at the history of all games to find similar previous games and determine the relative odds for each team. In football, a game in which the home team leads by 7 with 14 minutes left in the fourth quarter and the ball is around the 50-yard line would be as good as any other game. This ignores who is playing. Aaron Rodgers (back-to-back MVP of the league) as the QB of the home team compared to his return (one career start) Jordan Love as the QB of the home team at that time should be watched. completely the opposite of the same. The actual values of the game are important.
In today’s sports market, games are compared from then until the end of the tournament based on all the teams playing. This is less mathematical and more accurate.
A typical insurance policy can follow a similar pattern. All policies are divided into mixed groups and evaluated on large historical samples to determine the probability and risk of loss.
Data – we know a lot of business behavior before it’s written – and technology can now evolve to better understand the causes of damage process-by-process The level of difference is the wrapping of the homogenous group. Everything (and any combination of things) that is known about a process can be immediately analyzed for its vulnerability. This allows the automation of underwriting (as well as other actuarial calculations), which saves money and speeds up the process for both and to be accurate in its understanding of risks.
Start with the box score
Money ball focused on information stored for centuries. In fact, in 2018 the Supreme Court ruled that what happened in sports was public and not “private”. Innovation can be done on simple information.
In 2006, baseball installed cameras in every stadium to monitor the height, distance, speed and rotation of the ball by each player at all times (called “f/x” and now controlled by TrackMan). Basketball did the same with SportVue in its arenas and football now has chips on its shoulders. Sports have been enjoying telematics at the highest level for 16 years.
This first happened in 2019 (13 years later):
- The first model for predicting sports results based on telematics was proven to be the best model of simulation that only added to the content of the boxscore (ie public domain).
- The ball was able to reduce its bounds due to field tracking errors to less than +/- 3 feet (most were errors by a full court).
- The widespread adoption of fans, teams and players led to the use of telematics data in broadcasting.
Telematics is the future – emphasis on the future. We are just beginning the Moneyball era for commercial insurance. It will be important to collect and analyze telematics data, but it will take us 10-15 years to be able to use it in a way that adds value to the vast amount of information.
Recognition, which is available to the public to explain the right risk for the policy is a big step that insurance companies can make in its gradual change.
There may not be all video clips covered by insurance. Few people walk around wearing Progressive t-shirts or AllState hats. Calling receivers from carriers doesn’t attract thousands of fans who come in person. Insurance is not as exciting as sports. Maybe that’s why similar art has taken so long, but the time to do it is now.
Paul Bessire has been working in visual arts for 17+ years. He has used data and technology to become one of the world’s leading sports forecasters and is now focused on doing the same with other industries. He currently serves as vice president of data Coterie Insurance, an insurtech focused on making small business insurance faster and easier. Prior to joining Coterie, Paul was VP of Advanced Analytics at AMEND Consulting where he helped middle market clients – and other sports teams such as the Cincinnati Reds and University of Cincinnati athletics – make better decisions using data.
The opinions expressed here are the author’s own.