It finally feels like construction is having its own “Moneyball” moment.
I vividly remember the first time I saw the movie. If you’re not familiar with the film, which is based on the Michael Lewis book of the same name, it tells how the 2002 Oakland A’s general manager Billy Beane pioneered an incredibly successful analytic strategy for identifying undervalued players.
At the time, I was a project manager for PCL, and my immediate reaction was, “How can I use this to get better outcomes?” Today, I’m working at a software company that’s bringing AI and analytics to bear on safety in our industry, and, nearly a decade later, that question is still relevant to construction. How can we bring analytics to our business?
Truthfully, though, that’s not really the right question to ask. I recently had a conversation with Ryan Hale, CIO of Lithko Contracting LLC. about this very topic, and here’s what he had to say: “As technologists, we often discover an exciting new technology and then go looking for a way to use it. That’s backwards. The key is to first define the problem that you’re trying to solve and the goal you want to achieve by solving it. It doesn’t matter how much data or analytics power you throw at something if you’re pursuing the wrong goal.”
Moneyball and ‘good body’ nonsense
And that brings us back to Moneyball. Take a moment to watch the clip below (warning — there’s a bit of strong language, but nothing you won’t hear at any baseball stadium). It’s one of my favorite scenes, because it dramatizes so well the challenges we all face in bringing analytics into our organizations.
Beane, played by Brad Pitt, listens to his scouts describe the players they want to use to replace three stars recently hired away by far richer teams. The evidence they use to make their cases? A classic swing, a strong jaw, a lot of attitude. One tries to shoot down another’s pick by saying the player has an “ugly girlfriend” which he believes shows a lack of confidence.
So not only are they using anecdotal data, at best, to identify promising players, but Beane shows they don’t even understand the real problem they’re responsible for solving! They’re not replacing three players, and they’re not looking for a certain number of RBIs. The problem is that the Oakland As are one of the poorest clubs in baseball, but they’ve still got to create a winning team.
“Boston’s taking our kidneys, New York is taking our heart, and you guys are still talking the same old ‘good body’ nonsense like we’re selling jeans,” Beane says. “We’ve got to think differently.”
What does he get back from his scouts? A whole lotta pushback. Because after you’ve identified the problem and decide that analytics could help solve it, now you’ve got to overcome the internal inertia of “but that’s not the way we do things.”
It reminds me of a story my father-in-law, a long-time construction guy, once told me about how a company he worked for reacted to the introduction of the calculator. For most of us, it’s hard to remember a time before calculators were everywhere, but in the early 1970s, they were a new technology that not everyone fully trusted. For quite a while — and my father-in-law swears this is true — they had someone reproduce all calculations longhand, just to make sure the calculator was correct.
Today, there’s a mindset in construction that’s not that far afield from the norm in baseball scouting before Moneyball. As Ryan said during our conversation, “The modern construction industry has been around for more than a century, and a lot of the time we’re still using the same metrics to make decisions. Try to change that and you run into objections.”
Starting with the low-hanging fruit
So, where do you start? In my mind, the low hanging fruit is in safety. Not only is it a problem we know construction needs to address — nothing hurts like seeing one of your people injured on the job (or worse) — it’s also an issue about which we are already required to collect a ton of data that an analytics platform could use. Other possibilities include quality, productivity, and pre-construction. Again, these are areas on which most organizations already have a lot of data.
Now, how do you convince management to stop “talking that old ‘good body’ nonsense” and adopt an analytic approach? Here there are no simple answers, because every executive has different goals.
Let’s say you’re proposing to use Predictive-Based Safety (PBS) as part of a strategy to attain zero recordable incidents. Identify your management team’s hot buttons and push them hard. PBS works on a prescriptive model that provides a comparison of safety risk across all of your job sites to identify where an incident is most likely to occur, as well as concrete actions you can take to prevent that incident before it happens.
Is your management team focused on reducing the organization’s incident rate? Our customers using PBS see up to a 60% reduction in RIR in the first 12 months when compared to their safety performance before PBS. Is their biggest headache reducing costs? Nationally, a workplace fatality costs society an average of $1.42 million, but that’s just the direct costs. In construction, indirect costs from worker injury can be 17 times higher than the direct costs, meaning a death due to injury could cost more than $24 million. What if they’re concerned about winning more work? Owners are increasingly looking at safety records as part of their decision-making process as they shift toward best value selection methods. PBS is similar to the BIM models and 4D schedule animations of 10 years ago in that it can be used to “wow” owners in proposals and interviews. In fact, a contractor recently emailed me claiming that PBS played a part in helping them win over $1B in new work last year.
However you approach your management team, make it clear that analytics in construction is not a passing fad. After the Oakland A’s analytical approach to player management led to a thrilling 20-game winning streak and the American League West championship, other teams took notice, most notably the very wealthy Boston Red Sox. Two years later, the Red Sox used the A’s approach to win the World Series for the first time in almost a century.
MLB teams didn’t stop there. They continued to develop new applications for analytics in every facet of the game (think player pitch selection and defensive shifts) and improvement of their models continues today.
There’s absolutely no reason your organization can’t do the same.
Want to hear more from Tim and Ryan about Moneyball and how it applies to construction analytics? Watch our on-demand webinar today.