Popular film "Moneyball" made the baseball analytics revolution common knowledge with its depiction of the analytics-driven 2001 Oakland A's season, but Major League Baseball (MLB) has been collecting and using statistical information in this manner since the 1970s.
Advancements in storage capacity, wearable devices and computing bandwidth have further enabled baseball teams to capture and leverage big data sources, and the results are impressive. As noted by Deadspin, the "sabermetrics" revolution is now effectively complete. "Analytics is not some outsider's method of attacking institutional baseball; it's how institutional baseball does business." HCM leaders, meanwhile, find themselves in the adolescence of their industry's data revolution — what can they learn from baseball's big shift?
Why did analytics work so well for the Oakland A's? According to MIT Sloan School of Management, using hard and fast data about player performance — rather than relying on traditional methods of scouting talent — allowed general manager Billy Beane and assistant general manager Paul DePodesta to "find value where it wasn't readily apparent." Their work was based on sabermetrics, an objective analytics model created by baseball statistician Bill James. HR and finance departments now find themselves in a similar position with data about employee performance, job satisfaction and salary readily available, but in many cases lack an effective model of analysis.
For finance leaders, the rise of HR analytics demands a focus on objective measurements, which produce actionable results. More importantly, it demands a careful approach to spending. First, HR teams and C-suites need to identify their business objectives and utlilize their people data to get data-driven answers. Are employees likely to leave for other jobs? Are current salaries competitive? Next, identify likely data sources and invest in HR analytics tools that drive specific outcomes.
Stepping Up the Game
While big data analytics in baseball is largely done "behind the curtain," there's still room for improvement. The New York Times reports that hitting and pitching analysis now offers solid predictive potential, but fielding data remains less reliable. MLB Advanced Media introduced Statcast, which can track the movement of all players during a game and makes this information available to all teams after the last out. The result? Better predictions about fielding percentages and more accurate models of fielder performance.
HR faces a similar problem when it comes to pay equity. Often, you think you are paying employees fairly but data may reveal trends the naked eye can't see.
The Common Sense Strategy
Traditionally, baseball talent scouts used a combination of simple statistics and gut feelings based on years of experience to find the best players. Sometimes, this paid off for major league teams and sometimes top picks didn't perform as advertised. And while advanced analytics have reduced the chance of total failure, the unpredictable nature of human behavior means that no draft pick is a "sure thing."
The same can apply to recruiting. Seemingly "ideal" candidates may create corporate culture issues or deliver subpar performance based on their resume. According to Personnel Today, this stems from the nature of people analytics, which is "50% cold, hard statistics and 50% common sense." Just as baseball clubs haven't slowly reduced their reliance on talent scouts, HR departments can't rely entirely on data-driven answers to inform recruiting and retention policies. People analytics should help to inform the talent strategy in collaboration with the organizational expertise and insights your teams delivers.
Baseball analytics offers a solid starting point for HR finance leaders. Want to hit a grand slam for the C-suite? Draft business questions first, then spend specifically. Consolidate data where possible to drive better results, and apply common sense to any corporate analytics purchase.
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