5 MLB Analytics Practices That Work For Businesses
Best practices now embraced by baseball managers can be used by executives in all lines of business.
One of the most staid industries in the world is in the midst of a revolution. Major League Baseball, a notoriously slow mover rooted in centuries' old traditions, is radically changing in the way games are managed and players are evaluated, and the driving force behind the shift is data analytics.
Many of the best practices now embraced by general managers and managers throughout the sport can be used by executives in all lines of business. Let's take a look at five of the most critical practices.
1. Value Data Over Intuition
For decades, baseball managers made decisions almost entirely off experience and intuition. Having a hunch was a justifiable explanation for just about any decision, including the starting lineup, a pinch hitter or which relief pitcher to bring in from the bullpen. Nowadays, nearly every in-game decision is driven by volumes of data showing players' records and tendencies. If a manager makes a critical in-game decision, he'd better have a data-driven explanation. Most "old schoolers" who prefer making decisions on hunches have been pushed out in favor of those willing to embrace data.
The takeaway for business executives is simple: Be more data driven in your decision making. That doesn't mean past experience and intuition aren't useful, but be willing to take advantage of data you have at your disposal. Finding the right balance between data and intuition can make even polished executives much more effective.
2. Embrace New Metrics
Little more than a decade ago, only a small set of simple metrics was used for baseball player evaluations. For hitters, it was home runs, RBIs and batting average. For pitchers, it was wins and earned run average. But as technology made it possible to capture and evaluate more data, new metrics emerged that provide a more complete picture of player performance. It started slowly, with a handful of teams valuing on-base percentage -- the lynchpin statistic in the popular book Moneyball -- over the longstanding measure of batting average. It has taken off from there, to where a host of metrics, such as runs created and isolated power, have displaced traditional predecessors as the way to evaluate players.
It's time to embrace a similar approach at your business. Transactional data stored in relational databases -- the "batting average" of the everyday business -- still has a place, but if you're not also analyzing new forms of unstructured data such as text, video and social media, you're not getting an accurate picture of what's really happening.
3. Consider Context
Before the analytical revolution, baseball GMs looked at player performance in a vacuum. If a hitter had 100 RBIs, he must be good. If a pitcher won 15 games, he was a top performer. Over the years it's become apparent that average hitters can drive in 100 runs because they're in a lineup that creates an abnormally high number of opportunities, just as average pitchers can win 15 games if they receive an unusually high level of run support. Teams have learned that context must be accounted for, so they have started evaluating accordingly.
6 Tools to Protect Big DataMost IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.
Big Data Brings Big Security ProblemsWhy should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.
Join us for a roundup of the top stories on InformationWeek.com for the week of April 24, 2016. We'll be talking with the InformationWeek.com editors and correspondents who brought you the top stories of the week!