I was watching the 2011 movie Moneyball in the wee hours last night, and I was struck, anew, by one big question:

How did baseball writers evaluate what they were looking at … before the dawn of advanced analytics?

How did veteran reporters — including most of them right on through the Baby Boomer demographic — assess the 1927 Yankees or the 2004 Red Sox? How did they sort out who were the real heroes, and why, with statistical tools hardly any sharper than “batting average” and “runs batted in” and pitching record and ERA (earned-run average)?

I imagine there are legions of Millennials and Gen-Xers now covering ball who also wonder that, now that they are sunk in analytics up to their eyeballs.

The stat blizzard has been intense, the past 20 years, as a brief list of them would suggest.

From OPS to OPS plus, to FIP, xFIP, BABIP, Whip, aLI, WPA+, WAR, oWAR, dWAR … and dozens more, including “pitching velocity”, the rate of a ball’s spin out of a pitcher’s hand and “exit-velocity”, for which it appears that no universal acronym has yet to address.

Ball writers from the 19th century right on through most of the 20th could work with the handful of statistics that came their way, to try to explain the game, and they also could invoke the testimony of players and managers and general managers. They could compare contemporary stats, in that handful of categories that were followed … and make generalizations about what they had seen.

In the 21st century, baseball writers find that they have left behind an era of limited statistics, and now are nearly submerged by the flood of them that have come through in the past decade-plus.

Just this week, Sportsline.com announced a pitching stat of their own creation which they refer to by an acronym (ACES) … and I already have forgotten what exactly it is supposed to make clear.

Much of the stat revolution can be traced to Bill James, a fan and math whiz, who in the 1970s began thinking deep thoughts about what happened on the baseball field, and how the tools we had for evaluation were so limited, and how much conventional wisdom actually was conventional ignorance.

Within two or three decades he had convinced many/most professionals in the sports journalism biz that on-base percentage was a far more useful statistic than batting average, and that players who made lots of outs probably were not great players. Also, that certain time-honored baseball tactics were misguided: The sacrifice bunt rarely made sense, stolen bases were overrated, saves for pitchers didn’t mean much.

I remember a case in particular. Steve Garvey was a burly first baseman for the Los Angeles Dodgers in the middle-1970s, and later in that decade Bill James made a very strong case that Garvey (who was the National League MVP in 1974) was thoroughly overrated, in part, because he rarely walked and hit into a lot of double plays. His RBI totals were a function of teammates who had gotten on base — giving him lots of opportunities to pile up numbers that made him famous and wealthy — and overrated.

James came up with statistics that measured nearly every metric in the game. Most of his stats were eventually steamrolled by newer numbers as he moved into new areas of writing (True Crime, for instance) and others followed with evermore new stats purporting to illuminate this, that or the other.

What I wonder about is … if we go back 40, 50 years and look at the leading baseball writers for explanations on why Team X won 100 games and Team Y lost 100, would they have made evaluations based on the old dependables? Runs, home runs, RBI, batting average, won-loss record, total strikeouts and ERA?

The movie “Moneyball”, which gives us Brad Pitt as Oakland Athletics general manager Billy Beane, made it to screens in 2011, but the film is set in 2002, which already was semi-ancient history for the new armies of statisticians.

It did advance several new-ish lines of thinking that were caught up in Beane’s search for inefficiencies in the game, areas he could exploit as leader of a team with very little money: OBP (on-base percentage) was stressed, as was the wasteful nature of the bunt, and Beane had to fight nonstop with his tradition-bound scouting staff and their insistence that Beane did not know what he was doing.

The Jonah Hill “assistant” character, in the movie, seems to stand for all the big-brain stat wonks who were breaking into front-office positions, at the time, and can be seen as a composite character of revolutionaries like Theo Epstein and Paul DePodesta.

Eventually, Beane’s embrace of analytics led to a 20-game winning streak and a 103-victory 2002 season. Little is said about the Athletics’ strong pitching; scoring runs without spending much money is the basis of the movie and the name on the book.

So, how many baseball writers Got It Wrong, in adequately explaining what they were looking at, in an era before Big Data took over?

Probably a lot.

If someone were interested, they could pick out a baseball writer from the first 70 years of the 1900s, read his stuff and try to discern the real reasons for success or failure … and I’m guessing the odds are strong that the old-timers would focus on something that was not nearly as important as they thought at the time.

“Numbers” and “truth” do not always jibe. Lies, big lies, statistics, etc. But they probably are getting closer to it in 2019 than they have ever been, even if All Those Numbers sometimes seem to overshadow the humans who play the game and those who watch it.

I just wonder how many false/deluded prophets came before.