Digital Element Uses Big Data To Project Modern-Day Salaries For Baseball Greats Of Past

Digital Element Uses Big Data To Project Modern-Day Salaries For Baseball Greats Of Past

Digital Element Uses Big Data To Project Modern-Day Salaries For Baseball Greats Of Past

Gerrit Cole and Mike Trout have the highest salaries in the major league this year.

The New York Yankees right-hander and Los Angeles Angels center fielder will each make $13,333,333 in a pandemic-shortened 60-game season. That is a prorated 37 percent of what would have been their full salaries of $36 million if COVID 19 had not caused a major adjustment to the traditional 162-game schedule.

With that in mind, it’s fun to wonder about what kind of salaries stars of the past would command if they were playing today.

What would Babe Ruth’s value be in 2020? How much would a team pay Ted Williams? What kind of payday could Ty Cobb get?

Digital Element, a geolocation and IT intelligence company based in Atlanta, is using its big data expertise to determine the answers to those questions by pairing legendary players of yesteryear with some of today’s top players.

Rob Friedman, Digital Element’s co-founder and executive vice president, said “We’re used to dealing with huge amounts of data on a daily basis, so wanted to put our expertise into a fun scenario … to show the huge money in baseball today.”

Digital Element has used its experience in data analysis to discover how much some of baseball’s greatest legends would earn if they played the game in 2020.

The research pairs iconic players with their modern-day equivalents. Digital Element also places the iconic players with the team their equivalents played for in 2019.

For example, Babe Ruth is comparable to Mookie Betts. Thus, The Bambino would be a member of the Boston Red Sox instead of the Yankees.

The company has even produced a markup set of baseball cards picturing the legend in what would be their current uniforms.

Among the statistics used to determine a match between players are batting average, slugging percentage and RBIs. All stats are computed for an average of 162 games.

The analysts then took the percentage increase for the player’s strongest stat and applied that to the salary of the modern-day player in 2019, then, in turn, projecting an equivalent salary in 2020.

In a surprising result, the highest-paid player would be Stan Musial, whose current-day salary is projected at $50,172,414 while playing for the Detroit Tigers.

The St. Louis Cardinals’ legend’s comp is current Tigers first baseman Miguel Cabrera, an 11-time All-Star. Cabrera made $30 million last season, so Digital Element considers Musial to be 67 percent better.

Joe DiMaggio’s salary would be $40,537,634. The Yankee Clipper would be playing for the crosstown Mets because he is most comparable to Yoenis Cespedes.

Cobb, whose comp is the Los Angeles Dodgers’ Cody Bellinger, would be right behind DiMaggio at $39,428,571.

Ruth would check in at $33,575,980.

It seems almost certain Ruth would be the highest-paid player in the game in a real-life situation. However, being compared to Betts hurt Ruth from a financial projection standpoint.

Betts made $20 million in his second year of eligibility for arbitration, a lower salary than most of the current-day players used by Digital Element for its exercise.

Ted Williams was compared against Justin Upton of the Los Angeles Angels. Williams’ superior performance in RBIs over his 162-game average means he would earn $30,674,157 this season.

The entire list of projections can be found here.

It seems rather implausible that Musial would make nearly $20 million more than Williams if both were active players in 2020.

So, there are some flaws in Digital Element’s logic. Nevertheless, though it needs fine tuning, it makes for an interesting exercise during a spring and summer that has lacked baseball.

This article originally appeared on forbes.com To read the full article and see the images, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • zAdvanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards