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Analytics are now everywhere in Major League Baseball.
Every team has access to a wealth of data provided by MLB, which uses Google Cloud to power its analytics. Using Statcast, the platform MLB has developed using Google's analytics capabilities, teams can track players' every move on the field as well as the ball's movement from the instant it leaves the pitcher's hand to the moment a play is done.
It's with Statcast that teams can understand such information as the spin rate on a pitch, the bat path and bat speed of a swing, and the routes players take to track a ball when in the field and how quickly they cover the ground to make the catch.
So in an attempt to gain a further competitive edge, the Texas Rangers use Tableau in addition to what they're provided by the league to inform players and coaches in real time before, during and after each game.
Tableau, founded in 2003 and based in Seattle, is an analytics vendor whose platform is popular for its ability to make complex statistical analysis accessible to all through data visualizations. Tableau competes for market share with platforms such as Microsoft Power BI, Qlik, Oracle and SAS.
And with its ability to communicate complex statistical analysis in easily understandable formats, the Rangers are confident that Tableau gives them an advantage.
The Rangers do their machine learning in R, Python and Spark, store their data in Google Cloud and AWS, use SQL interfaces to interact with that data, and then ultimately use Tableau to communicate the data to the team's players and coaches.
"The competitive advantage is how this information is communicated to the stakeholders," Randall Pulfer, Major League analyst with the Rangers, said Wednesday during a breakout session at Tableau Conference 2022, the vendor's hybrid in-person and virtual user conference.
Randall PulferMajor League analyst, Texas Rangers
"If you can't take action from all the information you're gathering, it's useless," he continued. "Better decisions are the goal, and data and analytics are a means to that end, and we want to provide the ultimate decision-maker with more tools, more viewpoints, to make the most informed decision possible."
When statistical analysis beyond such conventional metrics as batting average, home runs and runs batted in -- and wins, earned run average and strikeouts for pitchers -- began to take hold in baseball around the turn of the 21st century, simply having an analytics department was a significant advantage for teams.
Even as the early years of the 2000s passed and teams such as the Oakland A's became renowned for their use of analytics -- particularly to find value in players other teams discarded, as depicted in both the book and film Moneyball -- some baseball clubs still resisted using advanced statistics and continued to rely more on traditional scouting methods to evaluate talent and build rosters.
Now, however, every professional baseball team has an analytics department and uses data to inform its decision-making.
It's why defensive shifts deployed to position players not in traditional spots on the field but in the areas where each individual batter most commonly hits the ball, once rarely used, are now ubiquitous. It's why teams' best hitters, in the past usually slotted third or fourth in the batting order, now often hit second. It's why pitchers attack certain parts of the strike zone with certain pitches in certain counts depending on the opposing batter's strengths and weaknesses.
And it's why defensive players can now be seen looking into their caps, where they have scouting reports and notes on the opposition, between pitches.
Gaining a competitive advantage with analytics, therefore, comes down to a team's ability to differentiate itself from what every other team is doing with analytics.
For the Rangers, their attempt to differentiate is with Tableau, in particular data visualizations using the vendor's Tableau Desktop and Tableau Server versions and the Tableau mobile app.
"It's about how we can communicate best, and that's where Tableau comes in," Pulfer said.
However, before gaining an edge with analytics beyond the technology provided by MLB, just as with any organization using data to inform decisions, baseball teams need to develop a decision culture, according to Pulfer.
They need buy-in from the top down, just as a Fortune 500 company does when it first uses analytics to inform corporate decisions after historically making decisions in more ad hoc ways.
"It goes a long way, when we're in the clubhouse talking to players and coaches, when the manager and front-office executives are supportive of what we're doing," Pulfer said.
Transparency, availability and consistency are also key to building a data culture within a baseball team, he continued.
That means data experts and analysts like Pulfer can't be holed up in an office five floors above the clubhouse poring over numbers as players and coaches attempt to digest the reports the team provides them on a daily basis.
The data experts need to be available to answer any and all questions in meetings and in the clubhouse to explain why a player might want to take a certain action, or why a certain action the player took in the previous night's game -- attempting to steal a base in a particular situation, for example -- was right or wrong.
And the data experts need to consistently deliver analysis in the same way so players and coaches can become comfortable with the team's culture of analytics.
"One of the most critical parts of my job is being available to players and staff," Pulfer said. "It's one thing to roll out a good process or metric, but it's another to see it through to make sure the end user understands it and is able to use it."
Finally, just as any organization needs to educate its data consumers before implementing a decision-making process with analytics at its core, baseball teams must explain their use of analytics, areas of emphasis for the season and why they think those areas of emphasis are important.
"We need to make sure they feel like they're part of the process and educate them on the importance of these numbers and how we think [the numbers] will help their career and help us win more games," Pulfer said.
Tableau in action
The Rangers use Tableau every day.
A normal game day begins about six hours before the first pitch, which is 1 p.m. for a typical 7 p.m. night game. At that time, Pulfer and his team have Tableau reports ready for players and staff based on the previous night's game and place the reports in lockers so players and coaches can look them over when they arrive at the ballpark.
In addition, the analysts make themselves available to answer any questions.
Those daily reports -- tailored to pitchers and position players based on their roles -- largely cover just the previous game and feature data visualizations to make the numbers easier to digest.
"We try to make those reports as simple as possible," Pulfer said. "The last thing a player wants to do, especially if they might have had a bad game, is look at a report that's full of numbers. We want the high-level takeaways."
Two hours later, generally around 3 p.m., meetings are held with different groups of players to go over strategy for the upcoming game -- for example, defensive positioning against certain hitters -- and review what happened the night before.
Two hours before first pitch, Pulfer and the rest of the Rangers' data analysts prepare materials for the players and coaches to take into the game, such as reference cards created in Tableau that players can store in their caps to quickly look up an opposing batter's tendencies or the opposing team's lineup with a short note about each player.
MLB allows teams to have iPads in the dugout during games that enable them to look up any report at any time, so the reference cards are designed for players and coaches to have in hand to provide a quick reminder.
"It's fun to make those cards and see them used, hopefully to help us win," Pulfer said.
Following the night's game, data analysts hold a quick debrief based on the data collected throughout the game, but with players and coaches anxious to get home, the brief reports built in Tableau just after the end of the game are developed so players can take them home and easily consume them the next morning.
"We're always trying to find ways to use data to our advantage, and we embody the data-driven culture," Pulfer said. "We're still improving, but every year we get better at it. It's easy to make things complicated, and difficult to make things simple, and our goal is to take a massive funnel of data and slim it down to the key takeaways players need on the field to perform."
Too much data in baseball can be dangerous -- it's counterproductive for a player with a baseball coming toward them at 100 miles per hour to be thinking about analytics. But using Tableau, the Rangers are able to deliver key data points in a digestible approach that enhances performance rather hinders it.
"Simplicity is the number one priority," Pulfer said.