As it turns out – a lot, actually…
For the entirety of its existence, baseball has been a ‘numbers game’. Compared to the other major professional sports, it operates within the most straightforward and easily relatable statistical universe. Our society takes comfort in the familiarity of baseball’s individual metrics: 3,000 hits; 500 home runs; a .300 batting average. As fans, we’ve romanticized these golden milestones, and Hall of Fame admission for big leaguers is largely dependent upon them.
But do these stats that we love and know so well really tell the full story? Do these ‘box score’ numbers explain the value of individual players? Can general managers rely on them to build superior rosters? Are we really that intellectually stale that we’re using 19th-century analyses?!
To answer the last question, in a nutshell – no. As popularized by Michael Lewis’s bestselling book, Moneyball, and the award-winning film of the same name, baseball has gone through a statistical transformation in the last couple of decades. Sabermetrics, the “search for objective knowledge about baseball”1, has led to the development of advanced metrics that have allowed us to better understand the value individual players add to their team. These days, every single pitch of every inning of play is recorded, giving statisticians a seemingly never-ending supply of data to work with.
As consultants, we constantly strive for “objective knowledge” of our clients’ operational and financial metrics. True, soft skills and gut feel are always part of the equation. But every recommendation we make, whether high-level or grittily actionable, must be rooted in objectivity. Fortunately, today’s technological environment allows us to realize this thirst for data.
Let’s look at sabermetrics’ poster child: wins above replacement, or WAR. Essentially, WAR captures an individual player’s overall value to his team. If Derek Jeter’s WAR for the 2009 season was 6.5, that means the Yankees won six and a half more games that year than they would have won if a replacement level player2 had played in Jeter’s place.
Let’s now look at batting average, the long-time darling of the box score. There is nothing wrong with batting average as a statistic. It answers the question of: “How often does a player get a hit compared to non-walk / non-sacrifice plate appearances?” Being extremely black-and-white, batting average does a fine job of answering that very question. It does not, however, answer the question of how good, or how valuable, a hitter is. It misses too much information, like the ability to draw walks, hit for power, etc.
The key to successfully understanding and applying analytics, whether in baseball or in business, is to make sure you are asking the right questions. We see this in consulting every day, with every client.
Consider a mid-sized manufacturing company that hopes to sustain its high growth trajectory. We might immediately be urged to find out everything we possibly can about this company. How will day-to-day swings in commodity prices affect its procurement budget? How quickly can finished goods be transported from coast to coast if its biggest distribution center experiences an ERP shut-down? How realistic are cost synergies when the time comes to acquire a competitor?
But are these really the right questions? Maybe short-term price fluctuations are negligible because the company is heavily invested in futures contracts. Maybe its disaster recovery protocol is relatively well-positioned, minimizing reliance on individual distribution nodes. Perhaps the M&A market is too bloated at the moment for a business of its size to bargain shop.
There is a clear takeaway: by asking the wrong questions, we are missing too much information. Worse, it could take us down the wrong path of discovery entirely, a huge loss of time. This easily avalanches into faulty analyses, missed deadlines, scope creep – in other words, an unhappy client.
Much like modern baseball, business is undoubtedly a ‘numbers game’. And more than ever, it’s easy to get lost in the vast sea of available data. Success depends on understanding what these numbers, as well as the important questions behind them, represent. Hall of Fame standards will challenge us upward, and justifiably so. Let’s not settle for a decent batting average.
1As originally defined by Bill James, widely considered the foremost sabermetrics pioneer
2A replacement level player is considered a slightly below average player in the major leagues