Data Science in Liga MX: Goals and Business Opportunity

Data science continues to make giant strides across all industries, especially with the widespread adoption of concepts like artificial intelligence (AI). Mexican soccer is no exception, but it hasn't fully exploited this field either.

While data science has been present in football for years, especially in player performance analysis and transfer suggestions, there are other areas whose potential has been explored only slightly, such as the creation of information bases that can impact marketing, merchandising, and other sources of revenue.

América is one of the clubs that places the most emphasis on data science in Liga MX talks. Recently, it secured its first three-time championship in the history of the short-league tournaments and increased its fan base to 45 million across Mexico and the United States, according to figures from Grupo Ollamani.

“Today, data science is a tool we use extensively, and it's combined with others for strategic decision-making. Together, it's what leads a club to success, or at least to trying to make as few mistakes as possible. We're convinced of that,” emphasized Héctor González Iñárritu, América's chief operating officer, in a recent conference with specialists from the Autonomous Technological Institute of Mexico (ITAM).

America is your partner for promoting the annual ITAM Sports Analytics Conference (ISAC), but does not receive software from that institution.

All you'll receive is help solving a real problem. The club will put it up for a student competition in November of this year, when the third ISAC event is held.

This connection led to an analysis of the data science landscape in Liga MX. According to a pair of specialists consulted by El Economista, half of the teams have such a department, although that doesn't guarantee that they're working properly.

Current situation

“I'd say 50% of the league is already doing data science at its clubs or using data technologies. Whether they're doing it well is another matter. What we can see from the outside is that there are two or three teams that are doing things very well, but it's still a very incipient industry,” said Santiago Fernández, co-founder of the ITAM Sports Analytics Conference (ISAC) and a specialist in data science applied to sports.

Jorge Dennis, co-founder of the data platform Statiskicks, had a similar opinion: “I would say that percentage is adequate. Obviously, there are clubs that have data science but don't use it optimally, because having it is one thing, and knowing how to use it is another. It also depends on the capital they have to include tools and the human capital they can hire.”

Data science in football is divided into two main branches: sports and business. The former has been the most notable, as it refers to the collection of data from matches and training sessions.

Within this sports branch, there are other categories: performance and scouting. The former is used to evaluate player performance, suggest strategies to the coaching staff, and even prevent injuries. The latter is used to analyze potential signings.

“Scouting through data is a first step, because it's done in stages, and the first advantage is having data to have a deck of options without having to travel or dedicate hours to games. Then, you can carefully rule out players. Today, there are many players that clubs have their sights set on, but they end up discarding them depending on financial demands and the coach's liking,” Jorge Dennis emphasized.

In the business area, data science captures the largest amount of information on fan profiles, ticket prices, product sales statistics, consumer preferences, and more. Everything is analyzed to offer better business options.

"There are very interesting cases, such as 601 Analytics, a company that began as an internal department of the Miami Heat (NBA) and later grew into a company that provides services to several teams in the top North American leagues. It provides data on ticketing, pricing, logistics, and users, and this can be used to generate more revenue," added Santiago Fernández.

Regarding the sports sector, experts agree that América is one of the teams that stands out the most in the use of data science. They also mentioned clubs such as Pachuca, Necaxa, Tigres, and Toluca, the reigning Liga MX champion.

Acceptance Challenge

“Teams that don't have data science are at a clear disadvantage. Before, it was the other way around: if you had it and the rest didn't, you were at an advantage. They're now understanding that those who don't incorporate technology—because it goes beyond data science to monitor physical status, health, tactical performance, and even business, operational decisions, and marketing—are falling behind. Mexico is still in a developmental stage where few clubs are successfully adopting these technologies,” the ITAM specialist insisted.

The main challenge sources see is changing the culture at clubs to accept that they need these tools and that their proper use can help inform their decisions.

“It's difficult to adopt artificial intelligence, the use of data, and all these technologies when you've been doing something manually, the old-fashioned way, based on intuition or traditional methods. Any technology has that adoption curve. Between the fact that it's difficult, new, expensive, and the cultural transformation, Mexico is a little behind European leagues, but it's one of the most advanced countries in the region, perhaps only behind the United States and Brazil.”

The Statiskicks co-founder emphasized that there are tools that, starting at $25,000 a year, can be used positively for small budgets.

But the difference also lies in who is in charge of these technological tools: "Having a data scientist, another data analyst who knows metrics and platforms, as well as someone for scouting, makes good use of the resources, although all of this depends on budget and management."

Dennis concluded: “It's increasingly important to get involved. I don't know how many years from now, clubs that don't prioritize this will be on the back foot, because there are more and more ways to measure situations that couldn't be measured before, and everyone can take advantage of it however they want.”