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Hok Talk: Analytics were not created to help front offices

But that sentence doesn’t tell the whole story

A stock image of a person in a suit holding up a posterboard with some generic graph stuff on it

Earlier this week, a small tiff was set off on baseball Twitter when former infielder Will Middlebrooks posted a tweet asking people to remember that analytics were created to work in the front office’s favor. Bill James immediately replied.

First, you must set aside the fact that Bill James didn’t create analytics anymore than I created the act of stuffing my face with Chinese food on a Friday night. You must also set aside that, despite his past actions, he currently works on behalf of a front office. The rest of that is accurate. Baseball analytics were largely created by fans and for fans to help them better understand and enjoy the game. Middlebrooks has since deleted his tweet and James has since walked back that first bit.

But the thing that likely set Middlebrooks off was Gerrit Cole talking about analytics during a recent interview:

“I don’t want to see that kind of stuff used to make the product worse or to manipulate overhead. That’s just stupid . . . I certainly don’t want to restrict somebody’s knowledge or trying to find some sort of an edge with numbers. They have a place in the game,” Cole explained. “When the analytics are used to suppress salary, or to manipulate service time, or for things other than trying to put the best product on the field, that’s concerning.”

Whereas Middlebrooks tweeted incorrectly, Cole seems to have hit the nail on the head. Numbers have a place in the game. No other sports fandom cherishes numbers to quite the same degree as baseball. What’s important is how the numbers are used.

1 - To Twist things

The thing about numbers being amoral is that they can be twisted to serve either good or evil. When they’re used to give a deserving player a job on your favorite team, that can be a good thing. When they’re used to justify fielding a worse team or keep a player in the minor leagues so you can pay him less money for longer, that’s not so good.

If you think analytics have no role in multiple teams every year announcing that their stud prospect needs to work on his defense in the minors for a couple of weeks before he can come up and play at the big league level, you’re wrong. If you think analytics have no role in teams realizing that they can get huge discounts on their players if they offer them an upfront sum that is significantly less than the player would be likely to make if they otherwise played out their rookie deal and arbitration years, you’re wrong. Even fans are in on it, now, calculating things like dollars-per-WAR to calculate if the GM made a good deal or a bad one.

There are more than just baseball analytics going on in baseball front offices, too. Teams get sweetheart deals with new stadiums combined with the ability to develop local real estate; they use analytics to separate the money they make from the real estate development and the money they make from baseball operations to justify not paying for more or better free agents. The Marlins’ fire sale following their purchase by Derek Jeter and company was a show of analytics making the case that they’d more easily recoup their costs by fielding an awful team that was cheap than a talented team that cost more money.

2 - To allow the team to play better

This is the marriage of scouting to analytics. For example, remember when Lorenzo Cain scored from first on that Eric Hosmer single to help the Royals win the 2015 ALCS?

Later we found out that scouts had noticed that Jose Bautista had a habit of missing his cutoff man and going straight to second on balls hit down the line like that. I don’t know if the Royals scouts calculated a percentage chance of that happening, but the advice would have been the same if they had. That’s analytics in action.

Another example of analytics and scouting working together is the recent trend of over-shifting against left-handed hitters. Only recently has this become a widespread phenomenon, but teams knew it could be an effective strategy for years and years before this; it was frequently employed against guys like David Ortiz and Jim Thome when I was younger. The analytics just showed that it wasn’t just the power guys who could be beaten with it. Everyone can understand why left-handed hitters might feel they’re under attack by analytics, though, right?

3 - To make better choices about who to pay

This is the Moneyball way: find a class of players with undervalued skill sets and pay them to play for your team. Don’t give big-money deals to guys who are unlikely to produce at a high level for whatever reason the analytics have revealed. No one is asking teams to waste money on players without doing any sort of due diligence. This sort of thing is mostly fine. This is just about being smart about spending money and adding the best talent to the team. These are things I think most of us can get behind.

Of course, since Billy Beane invented the concept, every team has built a massive analytics department of their own. Undervalued skillsets no longer exist in the same way. Because of this, the evolution of Moneyball has also just led to players being paid less. If you can’t pay less for a guy that no one else realizes is helpful, then why not just pay less for players in general? I wouldn’t be surprised to find that owners have told their GMs, “Billy Beane built a winner with half the money I’ve given you! Make it work!”

The MLB promise used to be that if you were a good player and a hard worker for the first six years of your major league career, you could get make up the difference. Now, the analytics show that players aren’t worth the large contracts they used to get when they finally hit free agency. We’ve entered a period where many players are underpaid as rookies and paid closer to what they’re worth as veterans, but no longer make up that early difference. It’s not hard to understand why they might feel cheated.

So, no, analytics weren’t created to favor front offices against players. Nor was a baseball bat created to favor me in my crusade to whack the heads off of all the garden gnomes in Topeka. But if I keep doing it for years and always justify it by saying, “Gnome Gnasher says it’s good for property values!” whenever I’m asked about it, people could probably be forgiven for thinking that the simple solution would be to just take the dang bat away.

Analytics weren’t created to help front offices, but they frequently have that effect all the same. Unfortunately, there isn’t a simple solution in this case. You can’t just remove analytics from baseball; it’s both impossible and unreasonable. And rules themselves aren’t to blame; it’s how they’re used. In the end, it seems like these things need to be taken on a case-by-case basis. Just like a rule could be added to eliminate the over-shift, rules can be added to force teams to put more money into the on-field product or changed to prevent teams from manipulating service time. If changes are going to come, they won’t come in the form of removing or changing analytics; that ship has sailed. They’ll have to come in the form of the players forcing the owners’ hands during negotiations for the next collective bargaining agreement, if at all.