The boldest prediction from Back to the Future II – that the Chicago Cubs break their century-old World Series curse in 2015 – did not come to fruition, with the disappointing yet lovable Cubbies falling to the New York Mets in the National League Championship Series.
But 2016 is a new year! And in the spirit of the fan base that braves impossibly brutal winters for a chance to cheer in Wrigleyville, let’s focus on the bright future of the club. They’re off to a blistering start to the season1. Jake Arrieta is a true ace, taking home the Cy Young award (for best pitcher in the National League) last year. Anthony Rizzo and Kris Bryant, the 20-something corner infield duo, will continue smashing home runs like it’s their job (which I guess it is). The list goes on…but I want to focus on a less talked-about subject from the 2015 season: manager Joe Maddon’s quirky practice of frequently batting his pitcher 8th.
To summarize 100 years in three sentences, traditionally in baseball, the pitcher is the last player in the batting order (i.e., 9th). This is fairly intuitive: you want your worst hitters getting the fewest at-bats over the long haul. The flip side, however, is that you want to maximize the runners on base for your best hitters (which, according to advanced metrics, should be 2nd and 3rd in the order). Thus, it makes sense to have a non-pitcher batting 9th.
While the strategy is not historically common – observed in about nine games per season since 1914 – over 20% of such games came last season2. Although the Cubs may get credit for popularizing the move, about one out of three teams in the major leagues used the strategy last year, clearing the previous high of five in 2009.
There’s an interesting parallel to the modern corporation. With the exponentially-expanding universe of hard data, initiatives to capture, massage and yield data-driven insights are becoming more and more popular. Sure, some companies manage and act upon data more effectively than others: Wal-Mart, for example, was one of the first big brick-and-mortar retailers to identify and act upon customer tendencies; Amazon’s and Netflix’s recommendation algorithms also come to mind here.
But not all corporate giants are so fortunate. For most of corporate America, there’s been a struggle to effectively deploy data and business intelligence systems and processes. The analytics domain is a daunting one, but in the 21st century, it’s a competitive imperative to understand and effectively leverage data. To generate long-term value, firms rely on more than just share buybacks and acquisitions. They need breakthroughs – whether strategic, technological, creative or through the smart use of data.
Back to the future (of baseball)
One might immediately conclude that we’ve made a statistical breakthrough on the pitcher-batting-8th strategy, inferring that such a dramatic spike in usage of the tactic is surely backed by results. After all, more and more baseball organizations are turning to Ivy League stat geeks (think Jonah Hill’s character in Moneyball) to quantify the subtle intricacies of the game, in favor of the “eye test” veterans (Clint Eastwood in Trouble with the Curve).
The verdict? According to some pretty daring mathematical models3, the expected advantage from swapping the pitcher into the 8-hole is 0.0039 runs per game, or 0.6 runs in a 162-game season.
So how do we tread this fine line – is the “way it’s always been done” a good enough answer?
Like many business decisions, it comes down to cost-benefit. In our example, it seems like adding a free 0.6 runs over the season would be wise. But who says it’s totally free? Maybe the Gold Glove second baseman who gets moved to 9th feels offended and drama ensues in the locker room. The dominoes fall in all the wrong ways and in a month, you’re replacing him with a minor leaguer just as the playoff race heats up. Was that infinitesimal scoring edge really worth it in the end?
C-Suite: the “so what?”
We see it all too often: the big data initiative. Executives will pow-wow and convince each other the only way to succeed is centralizing data points across their business universe into a state-of-the-art data warehouse. The push doesn’t stop there, though. Statistics PhDs show up in droves. Tableau and QlikView become the new business Bible. IT puts everything else on hold to build the CFO snazzy dashboards in SharePoint. Sure, it’s a massive undertaking, but data-mania gets you drunk with enthusiasm and long-term ambition like no other.
And then…nothing changes. Or there’s change, but you’re upset with the outcome. How? What?
There was nothing necessarily wrong with the project, and the mission was clear: “let’s effectively leverage data analytics.” A proper budget was allocated for the infrastructure, software and consultants. Middle management was on board, as well. And yet, the big data initiative failed.
With anything in the massive realm of “things enabled by high technology,” we need to have realistic expectations. You must be able to honestly answer that whatever you’re aiming for is a need and not a want. Calculating ROI is extremely important. Additionally, there are emotional, administrative and opportunity costs in addition to the implementation costs – the stuff beyond the numbers.
Clubhouse managers and the C-suite alike should be taking note. The pursuit of being a data-driven organization is without doubt a prudent decision. But the lesson here is that nothing comes free. As humans, we tend to copy our competitors without fully rationalizing why. We spend time learning and doing unproductive things; we procrastinate. We drink that sexy-looking Kool-Aid and slap words like “analytics” and “digital” on our websites.
With each new fiscal quarter, review cycle or baseball season, the pressure to effectively utilize data looms larger. Batting 8th or 9th doesn’t really matter – fully understanding the information you’re dealing with does.
When it comes to analytics, be “smart”, but be grounded…and the next hundred years won’t be so bad.