MLB Analytics : Benefiting Businesses

MLB Data

This post marks the beginning of the second topic I will discuss in my blog postings; the use of advanced analytics in the MLB to gain insight into both gameplay and actual player value. Previously, I had discussed how the MLB’s model for social media could be applied (and would be beneficial) to almost all firms in today’s business climate. In a similar fashion, I will discuss how it would be prudent for businesses to adopt 3 particular mindsets/ analytical practices that the MLB is using to decipher big data. Many of the best practices now embraced by GMs and team managers throughout the sport can, and should, be used by executives in all lines of business.

1.) Value the Data


For the majority of its vast history, baseball managers have made decisions almost entirely off experience and ‘gut feeling’. This gut feeling was a justifiable explanation for just about any decision, ranging from the starting lineup to a pinch hitter or which relief pitcher to bring in from the bullpen. Now, with the understanding of big data through advanced analytics, nearly every in-game decision is driven by longstanding data records showing players’ statistics and tendencies. If a manager makes a critical in-game decision in baseball’s modern era, he’d better have data-supported reasoning.

The business takeaway of this is simple, be more data driven in decision making. Not saying that past experience isn’t important or applicable, but a willingness to take advantage of data you have at your disposal must be present.

2.) Be Open-Minded to New Metrics & Methods


Traditionally the hitting aspect of the game was solely evaluated on home runs, RBIs and batting average. For pitchers, wins and ERA dominated the scene. Nowadays, new metrics have been introduced that have changed how we have traditionally labeled a player as “effective”, and this has sparked quite the debate. It started slowly, with a handful of teams valuing on-base percentage (the prevalent statistic in the innovative book Moneyball) over the longstanding batting average. This has placed enormous value on players like Mike Trout, who thrive on the basepaths.

A similar approach needs to be embraced in the corporate realm. Traditional data does have its place, but the analysis of new forms of data such as text, social media, etc… are available that give an even more accurate picture of the climate of an industry.

3.) Prediction


Baseball teams have historically scouted, evaluated, and compensated players based on their production in previous seasons. The better a player played in the past, the greater his salary in the present and future, when contracts began to lengthen in years and ‘guaranteed money’ became a thing. By the end of a lengthy contract, teams were often overpaying for players whose best days on the diamond were well behind them (think Carlos Zambrano for all you Cubs fans out there). However, the increased use of analytics has enabled teams to become better at evaluating a players’ future performance (and when they might plateau), and structuring their contracts in accordance.

Likewise, businesses should combine traditional business intellect with predictive analytics. With so much big data now available to be interpreted, companies can make informed decisions not based on what has occurred, but what’s most likely to occur based on the trends data can illuminate.

Each of these embraces of modern analytics can be as beneficial to the corporate sector as they have been to America’s past time. All it takes is an inquisitive mindset, and a little deviation from the status quo, to gain lasting competitive advantages and industry insights.

Drive Home Safely,

Bryan White, MBA

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