The Major League Baseball draft that was the basis for the book Moneyball took place in 2002. The book itself was published the following year, and a year after that came the paperback edition, in which author Michael Lewis wrote an afterword that focused on the surprising reaction it had touched off.

The examination of how the Oakland A’s were challenging baseball orthodoxy had been blasted by many writers, executives and players, from Joe Morgan to Pat Gillick, and a debate of sorts ensued. “It wasn’t as interesting as a real debate,” Lewis wrote, “in that there was no chance for an exchange of ideas. It was more like a religious war — or like the endless, fruitless dispute between creationists and evolutionary theorists.”

Much like a religious war, that debate has proved to be stubborn and intractable.

The last 15 years have included many moments in which the arguments over the use of analytics in sports decision-making should have been put to rest. Every kind of sports organization has come to understand the importance of data analysis when making front-office decisions, and while some have embraced it far more than others, there are enough examples of the successful application of the Moneyball effect — using data to create a competitive advantage — that the debate, such as it is, ought to be over.

Spoiler alert: it isn’t. Rarely does a week go by when someone doesn’t take a shot at analytics, or when some kind of development with a team isn’t seen as an indictment — or an exoneration — of its reliance on data.

Edmonton Oilers coach Todd McLellan recently defended defenceman Kris Russell, who has become something of an avatar for the stats-versus-scouts debate, by saying: “I know all the analytics nerds out there find ways to run him into the ground, but he means a lot to our team.” Having successfully deployed the nerd jab — it’s either that or mention the word “computer” derisively — McLellan said his players all understood Russell’s importance. “So analytics that if you want,” he said. Bam.

McLellan has long been a data skeptic — “the best analytics is a set of eyeballs,” he has said — and he at least has a like-minded general manager in Peter Chiarelli, who among other things has traded Taylor Hall and acquired Milan Lucic, both moves that set off the analytics-community equivalent of a bomb-shelter klaxon. Elsewhere, the Arizona Coyotes and Florida Panthers both fired coaches in the past year who were not on board with data-driven plans.

In the NBA, the Philadelphia 76ers split with former GM Sam Hinkie when ownership lost patience with his much-ballyhooed, analytics-inspired rebuilding “process,” only to see much of Hinkie’s mad-scientist routine finally pay off this season, long after he left. And in the NFL, the Cleveland Browns fired Sashi Brown, one of their statistics-oriented architects, in the midst of a winless season. It’s unclear what will happen to Paul DePodesta, the former baseball executive who was brought in to Moneyball the crap out of the Browns — DePodesta was one of the key characters in that Oakland A’s story 15 years ago — now that the football team has installed John Dorsey, a football guy, as general manager.

These things happen, a team hiring or firing someone or just a snarky aside from a coach or commentator — TSN’s Pierre McGuire noted that “analytics don’t measure heart, courage or desire” when asked about Russell — and it’s like a spark on the smouldering embers of the debate. Suddenly everyone is having the same arguments again.

The puzzle in all of this is that there ought to be plenty of common ground for both sides to hammer out a permanent detente. One illustration of how sports has embraced numbers comes annually at the Sloan Sports Analytics Conference in Boston. Co-founded more than a decade ago by Houston Rockets general manager Daryl Morey, it has grown from 100 or so people on the MIT Sloan campus to more than 4,000 attendees at a large Boston conference centre. Last year representatives from more than 130 pro sports team made the trek to Nerdstock. The buy-in is obvious.

One of the curious things about Sloan, though, is that even with all the true believers in attendance, there is always a lot of talk about missionary work: how to get the wider sports audience — and those in the industry — to understand what it is they are doing. Speaker after speaker will stress that they never claim to have all the answers and that there is absolutely a role in sports for things like traditional scouting, and the point is made that all analytics types are trying to do is provide more information to help coaches and executives make important decisions. After all, who wouldn’t want more information?

Most organizations concede this point, although they all place different weights on the information they get from data analysis, scouts, and in-person research. To the extent that there is a debate in front offices, it’s about finding the right mix.

Brian Burke, the Calgary Flames president who has taken his share of shots at nerds over the years, will say in one breath that the Flames have “the best analytics guy in the business” and in the next that he values that information less than that gleaned from scouts and from talking to prospective players. Morey, meanwhile, has said that he wouldn’t interview any potential draft picks if he could get away with it, so little faith does he have in the value of that process. (He does them because he knows he will be fired if he drafts someone he has never met and then that player, for example, immediately quits and declares painting his true love.)

But if the argument over analytics is just one of degrees, why does it still engender such ill will?

I think it goes back to that thing about religion. Put another way, as much as there is acceptance now of the value of data, often what that data says is something that pushes back against deeply held beliefs. In baseball, it can tell you that a pitcher with a 15-6 record was still objectively worse than someone who went 8-10. It basketball, it can tell you that someone who averages a team-leading 23.2 points per game is much less valuable than the teammate who scores 15.3 points and tries on defence. And in hockey it can tell you that the defenceman who dives in front of a lot of shots and works really hard nevertheless has a negative impact on his team’s performance.

Sometimes these are things that coaches and managers — and fans, definitely a lot of fans — just do not want to be told. I once had a long discussion with a former major leaguer-turned-analyst about how pitching wins were affected by so much not in a pitcher’s control, especially in today’s game where bullpens have such a heavy workload. “Give me the guy who wins,” he said at the end. It was like an atheist talking to a priest.

With all those ingrained beliefs still out there, it doesn’t take much for new-school thinking to be tossed aside. One of the underpinnings of the statistics movement is the importance of sample size: don’t make decisions based on a small data set, because they are bound to be affected by randomness and luck.

But humans love to believe in small numbers. Analytics might say that a given team is doing all the right things to win in the long term, but tell that to a coach in the middle of a losing streak: he will find little consolation in a run of bad luck. There’s a conflict there between sports and math that might never be truly resolved. It says a team can never fully account for randomness, and so is better off focusing on the process of team-building.

They give out the trophies, though, for results.

• Email: sstinson@postmedia.com | Twitter: @scott_stinson