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Top 25 Baseball Stories of the Decade — No. 2: Analytics goes mainstream

2019 AL Wild Card - Tampa Bay Rays v. Oakland Athletics

OAKLAND, CA - OCTOBER 02: Executive Vice President of Baseball Operations Billy Beane of the Oakland Athletics looks on during batting practice prior to the AL Wild Card game between the Tampa Bay Rays and the Oakland Athletics at Oakland Coliseum on Wednesday, October 2, 2019 in Oakland, California. (Photo by Daniel Shirey/MLB Photos via Getty Images)

MLB Photos via Getty Images

We’re a few short days away from the dawn of the 2020s. So, instead of counting down the Top 25 stories of the year, we’re taking a look at the top 25 baseball stories of the past decade.

Some of them took place on the field, some of them off the field and some of them were more akin to tabloid drama. No matter where the story broke, however, these were the stories baseball fans were talking about most over the past ten years.

Next up: No. 2: Analytics Goes Mainstream

The collection and study of baseball data, and the application of lessons learned from that data in baseball coaching and decision making -- something, for these purposes, we’ll call “analytics” -- is not new. It was not invented by Bill James in the 1970s or Billy Beane in the early 2000s or whatever else people who only know a little bit about it all often say. Indeed, it’s been around since almost the moment the game began:

  • The Knickerbockers kept track of rudimentary play and game results back at Elysian Fields and Henry Chadwick is credited with inventing box scores and inventing stats like batting average before the dang Civil War even began;
  • In 1919 National League President John Heydler hired the Elias brothers -- namesakes of the still-in-existence Elias Sports Bureau -- to maintain official playing statistics;
  • F.C. Lane, the editor of Baseball Magazine, published a book in 1925 called “Batting” that contained a treasure trove of interviews of players like Ty Cobb, Cy Young, Babe Ruth and Walter Johnson, each of whom gave specific insight, tips, and advice about the art and science of their games;
  • In the 1940s Branch Rickey hired a statistician, Allan Roth, to evaluate player performance for the Brooklyn Dodgers;
  • Beginning in the 1960s Orioles manager Earl Weaver used index cards to fine-tune platooning systems and pitching change strategies; but
  • Weaver had his limits, though: his second baseman, Davey Johnson, wrote a computer program for the express purpose of convincing Weaver, statistically speaking, that he should bat second. Weaver didn’t buy it. Johnson, however, would continue to use computer programing to help analyze the decisions before him as a minor league and major league manager.

You might not think of a lot of that kind of stuff as “analytics” as I defined it above, but you’d be wrong. Knowledge, in and of itself, is power, and all of that as much as 170-year-old work increased baseball knowledge and allowed the people who ran teams, the players, the reporters and the fans to get a handle on the quality of a player and the quality of a team. And, of course, once one has a handle on such things, one can’t help but make decisions based on such information. That’s analytics, jack, even if you want to call it old school.

From there you probably have a decent idea of how things went.

Bill James published his first Baseball Abstract in 1977 and gave what he coined “sabermetrics” increasing mainstream exposure into the 1980s. Pete Palmer simultaneously began his revolutionary work in creating statistical databases -- now stats could not just be complied and referenced, but recalled and crosschecked via any number of filtered interrogatories, increasing the utility of statistics exponentially-- and, in 1985, he and John Thorn published the essential Hidden Game of Baseball. On the shoulders of those giants came Craig Wright, Rob Neyer, David Smith, Nate Silver, Sean Forman, Tom Tango, Voros McCraken, Dan Szymborski, Jay Jaffe and a host of random fellow travelers and sabermetric-adjacent writers who have haunted the Internet for some 25 years now, some of whom even manage to get paid for their scribblings by large multimedia companies even if they’re not very good at math.

But despite that line of knowledge and insight stretching back as long as it did, the adoption of what people tend to think of as advanced analytics -- the stuff from, say, Bill James and Pete Palmer onward that was consciously identified as sabermetrics or something like it -- was pretty slow on the part of baseball clubs, the baseball press, the fans and Major League Baseball as a whole. That Craig Wright fellow I mentioned above was the official team sabermetrician of the Texas Rangers in the 1980s and, of course, the Oakland A’s under Sandy Alderson and then Billy Beane went into the advanced analytical woods full bore, but it was still pretty fringy stuff in the grand scheme. Things my better-at-math friends were talking about on websites on which I spent way too much time.

Moneyball, the book, came out in 2003. Like a lot of mass market books it, in some ways, reflected a moment that was already gone. The real period during which the Oakland A’s had a significant advantage by doing stuff no one else was really doing probably came between 1997 and 2001, just before Michael Lewis began writing the book. By the time the book came out it was probably fair to say there were 3-5 teams, at least, exploiting advanced analytics. As the aughts wore on the comparative advantage teams using “Moneyball” thinking enjoyed began to wane. If I had to guess I’d say that something like 75% of the league’s front offices had dedicated analytics departments by around 2010 or so, and that could be an underestimate.

Still, the idea was enough of a novelty that, when the Moneyball movie came out in 2011, it inspired conversations and/or arguments among people who worked in, enjoyed and covered the game about the proper role of statistics. As late as the early part of this decade some teams were still making player transactions that led one to wonder if they had been caught in a time warp that left their front office stranded back in 1979. It was still not unusual for there to be “stats vs. scouts” arguments, to read articles with the headline “WAR, what is it good for?” and for teams to, at least publicly, diminish the role of their analytics departments. There were still stories about teams hiring bright young general managers who had to, on their first week on the job, dust off obsolete computers to find the most basic information about the roster and farm system they were taking over and it was still not uncommon for a team to make a big deal out of hiring some random internet analyst to help them make up for lost time.

And then, at some point in the past few years, the argument seemed to end. Everyone was using analytics of some form or another and most people were pretty cool with it, even if they themselves didn’t get too deep into the math. I’m not sure when, exactly, it happened. I just looked up not too terribly long ago and realized that, for the most part, all of that old debate was a thing of the past.

Part of it was a matter of simple evolution. Some of the more vocal anti-sabermetric voices in the media retired, were marginalized, or moved on to other things. People who were resistant to advanced analytics within baseball front offices or on scouting staffs either got with the program or found themselves out of work. Most TV broadcasters who weren’t local legends likewise did not have the luxury of maintaining an adversarial stance. Not when their production staff had more data and more graphics than they knew what to do with and seemed positively hellbent on cramming as many of them into a TV broadcast as was humanly possible.

But it wasn’t all just “accept it or die.” Some of it was a simply matter of the conversation about analytics getting quieter and, in some ways, more user friendly.

As the decade wore on, teams began to develop their own proprietary metrics and analytical tools as opposed to using metrics and tools that were freely available to the public. As far as teams were concerned the less they talked about what they were doing the better. That continues to frustrate independent analysts and analytically-oriented fans, the sorts of which helped popularize advanced analytics to begin with -- something that was largely open-source went closed-source and certain sorts of people really hate that -- but it also had the effect of lowering the temperature on the discourse, if you will. If you can’t tell what the Mudville Nine was thinking -- and if the GM of the Mudville Nine simply says oblique things about how he and his staff know more than fans so, perhaps, they should not judge -- there’s not much to argue about.

Maybe the most significant analytical development of the decade came in 2014. That’s when Major League Baseball introduced Statcast, its own proprietary radar and video-based data collection system in which the position and movement of every player and the ball could be recorded and analyzed. This was big data on a big scale. It went wide, to all 30 MLB parks, in 2015.

Statcast has both public-facing and an inward-facing sides. On the one hand, the system collects reams and reams of data, furnishing it to the 30 clubs, each of whom do their own things with it internally, using it to evaluate players and in-game decisions. This part of it is itself all rather oblique. Once in a while, you’ll hear players themselves talking about how they’re not entirely sure what goes into it. Other times you’ll hear players talking about how insights gained from Statcast helped them improve their game. Which of these opinions is voiced, one suspects, hinges upon whether or not the player was told things they did not want to hear about their performance or their value by the front office.

On the public-facing side, Statcast has led to the release of a handful of new statistical categories for fan consumption, such as exit velocity, launch angle, spin rate, first step, route efficiency and the like. Some of the things measured by Statcast have been studied by analysts for some time -- and it’s not always clear if fans are getting something approaching raw data or something packaged for easy TV and Internet consumption -- but they have largely been accepted by fans as part of the baseball landscape in ways that far simpler concepts or statistics never were a mere 10 or 15 years before. Back then, those things would’ve been railed against as the creation of some geek living in his mother’s basement. Today broadcasters will casually mention launch angle or exit velocity like the used to mention batting average.

But the mass acceptance and ubiquity of advanced analytics in baseball has not been all Skittles and beer. There has been a downside to it.

We live in a baseball age in which cutting edge data influences almost every decision, and those processing that data for major league baseball teams all claim to be doing their own special, secret and innovative stuff. But, in practice, that cutting edge data seems to be, basically, telling every team the same basic things, and not all of those things make for good baseball or, in some cases, good behavior:

  • Things like “letting the other team put the ball in play is a bad thing” and “hitting homers is the best thing,” which has led to a lot of strikeouts and walks and to the least amount of actual kinetic baseball activity and the most amount of dead time in the game’s history;
  • Things like “run prevention is everything,” leading to a revolution in defense and pitching scouting and pitching strategy that, at the outset of the decade, led to something akin to a new deadball era, but which was replaced -- possibly artificially by the league as a reaction -- by a juiced ball era, which has given us whiplash extremes in the space of ten years;
  • Things like “the more fresh arms thrown at the other team the better,” which has led to an era in which the bullpen has an insanely outsized importance and lot of pitching changes that lead to increasingly longer games;
  • Things like “younger, cheaper players are more efficient uses of resources,” leading to downward pressure on salaries and the casting off of veteran players, even if they are fan favorites, and the increasing accrual of baseball revenues to owners’ pockets as opposed to players’ pockets;
  • Things like “any little edge -- including stealing the other team’s signs -- can make a difference” leading to what’s going on with the Astros right now;
  • Things like “the playoffs are a crapshoot, so it’s better to do a total tear-down rebuild than to try to compete with anything less than a super team,” leading to the tanking epidemic;
  • Things like “the most important thing is unity of philosophy and everyone following the lead of the head of the baseball operations department,” leading to a disturbingly homogenous front office culture which, in turn, has led to disturbingly homogenous coaching, scouting and managerial staffs, because those Ivy League guys with the business and analytics backgrounds at the top of the baseball operations department tend to like to hire people like themselves.

If you’ve been following this countdown [see previous entries below], you’ll note that almost all of those points led to one of our Top-25 stories of the decade, many of them dealing with developments that are bad or, at the very least, problematic for the game.

Which should probably cause the powers that be in baseball to begin a process of self-examination as to whether the scientifically-derived efficiency gains that were once the bright and sunny promise of the analytics revolution are, actually, any different in value than that which came before. Whether the optimization and efficiency that revolution has promoted has caused us to lose some things from the game that matter. Things like variety. Action. Diversity. Fun. Maybe even humanity. And maybe they should ask themselves whether a moral, ethical and aesthetic check on the work they have done and the innovations they have fostered is in order.

I have no idea if people inside the game will do that -- we’ve been waiting for Silicon Valley to examine the societal consequences of its innovations for some time too and, so far, no dice -- but it cannot be said that the mainstreaming of analytics in baseball in the past decade has not been a massively consequential story, no matter how it shakes out ethically.

It’s the number two story on our list, in fact. Number one is up next.


No. 3: Baseball Teams Become Cash Cows
No. 4: Bud Selig Retires, Rob Manfred takes over
No. 5: The Tanking Epidemic
No. 6: The Deaths of Young Players
No. 7: Miguel Cabrera Wins the Triple Crown
No. 8: The Biogenesis Scandal
No. 9: Bullpen Mania Takes Over the Game
No. 10: The Rise of the Young Player
No. 11: Baseball Goes From Deadball To Juiced Ball
No. 12: Baseball Begins Rewriting the Rulebook
No. 13: Baseball Adds a Second Wild Card
No, 14: Albert Pujols Signs With the Angels
No. 15: Baseball Continues a Remarkable Run of Labor Peace
No. 16: Baseball implements a domestic violence policy
No. 17: Cardinals Employee Hacks Astros’ Database
No. 18: Frank and Jamie McCourt Bankrupt the Dodgers
No. 19: Baseball Embraces Gambling
No. 20: The Hall of Fame Logjam
No. 21: The Bat-flippers Win the Battle Over the Unwritten Rules
No. 22: Astros switch leagues
No. 23: The Strasburg Shutdown
No. 24: Chicken and Beer
No. 25: All-Star Game no longer counts
Honorable mention: Astros Sign Stealing Scandal

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