Santos Laguna filters talent with Analytics: Gonzalo Pineda reverses the scouting process

In an exclusive interview with RÉCORD, Gonzalo Pineda used exactly the two verbs I always train my ear to catch: "explore" and "developing". The Santos Laguna coach described a scouting system supported by Analytics and artificial intelligence, covering both the first team and the Fuerzas Básicas academy. For someone who reads transfer money flows for a living, that statement belongs to the level of method.

After years of reporting for the French market, I keep the habit of replaying press conference tapes twice. The first time to collect figures, the second to hear whether the speaker truly believes what he is saying. When Pineda described Analytics as a filtering tool, I heard the caution of a man who has stood on a technical touchline. Behind it sits a familiar economic question: which club pays for the act of finding players, and how?

Liga MX has never been naive about data. Big Mexican clubs have long hired analysts, bought international data packages and built video rooms capable of tracking both the domestic league and youth competitions across the Americas. Most of that work, though, stops at post-checking: understanding a player the coaching staff already spotted, or building an opponent report before a matchday. Using an algorithm as the first filter, before sending anyone to watch live, is a different step in kind.

Santos Laguna occupies a position that makes this move structurally sensible. The club from Torreón, an industrial centre in northern Mexico, is not among Liga MX's biggest spenders, nor does it enjoy the urban pull of Mexico City or Monterrey. In places like that, the only competitive edge you can buy with less money is a process edge. Pineda said the system will cover both the first team and Fuerzas Básicas, with the scouting network expanding across Mexico and the United States.

Mexican competitions carry mechanisms that encourage the use of young players, and any such mechanism raises the value of an efficient development pipeline. For a club that cannot buy stars, investing in the process of finding players is the least risky item in the entire budget.

Two names he mentioned deserve recording: Omar Tapia and Andrés Bejarano, the men building the observation network. In my trade, when someone names people rather than concepts, that signals a plan with staffing behind it, not just a slogan. The US expansion is the most important technical detail in the whole story.

One more structural point: this network is not only about finding players for the youth ranks. Pineda made clear that the first-team signing area also receives technology support, meaning one data pipeline serves two purposes — discovering players for the academy and verifying players for the senior squad. At many clubs those two departments work separately, even competing for resources. Merging them into a single pipeline is a way to cut costs without cutting budget.

The core lies in the order of operations. In the traditional model, the live watcher is the first filter: a scout sees something, writes a report, and only then does the club dig deeper. The error sits in the human eye from step one, and the cost of that error does not stop at a bad contract. It also means thousands of travel hours, flights and hotel nights poured into tracking the wrong man.

Pineda reverses that order. Analytics serves as the primary filter: the algorithm scans a wide player pool, narrows it into a shortlist, and only then are humans sent to watch live. He never spoke of replacing people. He called Analytics a filtering tool, with direct observation still holding the final say. As system design, that is a sound choice: it reduces the risk of purely algorithmic decisions while exploiting what machines genuinely do best — scanning volume.

The economics of the model are where the real discussion sits. The club published no budget, named no data vendor, offered no timeline and no measurement metric. The underlying logic is still readable: a data-driven filter combined with an in-house academy and a cross-border scouting network is a capital-efficient model. It replaces part of the need to buy established players with the cost of searching, and searching is always cheaper than buying wrong. Reading a deal does not require listening to rumours, only watching where the money goes — here, the money is flowing into process, not into players.

The US direction shows where they are aiming. The American market holds a systematically undervalued group: young Mexican-heritage dual nationals raised in MLS academies, fluent in English and Spanish, yet overlooked by European clubs. They sit between two scouting systems, and the gap between those systems is where value gets forgotten. A data filter capable of scanning that group will save Santos Laguna a great many flights.

At the other end of the system, Pineda mentioned young names. He said the club has good talent right now, and that U-19 and U-21 players have already had approaches with the first team and are constantly monitored by the coaching staff. For an academy, that is a stronger signal than any report: the distance between youth and senior football is being shortened by constant observation, not by a ceremonial debut.

Based on my experience following matches in Liga MX and youth competitions across the Americas, a first-team coach stating plainly that he is watching U-19 and U-21 players is a rare signal. It means the data system does not sit in a detached analysis room, but is wired into weekly professional decisions.

What the official story has not touched is the maturity of the tool. Pineda used the words "explore" and "developing", meaning the system is most likely still in a testing phase rather than fully operational. No vendor name, no budget, no conversion metric from shortlist to contract. Without those, the word "AI" in a press release can be a handsome banner rather than a running machine.

The structural risk sits in the input data. Youth player data across the Americas is far thinner than professional European league data. However good the filtering algorithm, it is only as good as what it receives, and at U-19 and U-21 level the coverage is rarely enough to separate a player who will explode from one who simply matured early. If the filter misses exactly the profiles a human eye still catches, the club saves money on searching and pays for it in misses.

Another fragile point: the project rests on one person. Pineda is the one announcing it, and when a project carries a name, it lives and dies by that name's results. If first-team results slide, the data system will be used as a shield or as a target. A contract is the record of greed, but also the diary of hope — and here, the contract being signed is with a process, not with any player.

France Bleu taught me one thing: unverified, never on air. At 46, I no longer chase breaking news; I chase verified truth. For Santos Laguna, that truth will not sit in a press release, but in the first name this system finds before every other scout.

What is worth watching is whether the system produces measurable difference. If Santos Laguna signs a player no rival scout had on their list, the model has proven itself. If not, Liga MX still faces an old question: does data help find players better, or only help explain decisions already made?