MLB's Search for New Fans: AI and Social Media as the New Frontier of Sports Marketing

Major League Baseball (MLB) is looking for a new kind of fan. As the playoffs get underway, MLB posted a video on Instagram and TikTok to drum up support for the teams competing for the World Series. The video features players from the Milwaukee Brewers, Los Angeles Dodgers, Chicago White Sox and other teams sliding into home to score and other highlight clips. What looks like routine social media management actually reflects professional sports leagues' deep anxiety about fan growth and their strategic pivot.

Social Media Marketing: From Broad Reach to Precision Targeting

MLB chose to release this video at the start of the playoffs, timing it precisely. The playoffs are the most-watched window of the baseball season and a key moment for converting casual fans into core fans. However, simply posting content on official accounts is no longer enough to break through on information-overloaded social platforms. What MLB needs is a smarter, more personalized content distribution strategy.

According to industry observers, MLB has been steadily ramping up data analytics and artificial intelligence in recent years to understand fan behavior and optimize content recommendations. For example, by analyzing users' interaction patterns on social platforms, the league can determine which teams, which players, and which types of clips resonate most, and adjust its content production accordingly. This data-driven approach is the core method behind "finding new fans."

How AI Is Reshaping the Fan Experience

Professional sports leagues' use of AI goes far beyond content recommendations. From personalized ticketing recommendations and dynamic pricing to real-time data visualization during games and augmented reality viewing experiences, AI is permeating every stage of the fan journey. For MLB, the relatively slow pace of baseball and its complex rules create a natural barrier for new fans. AI is well positioned to lower that cognitive threshold.

For instance, through natural language generation technology, the league can automatically generate easy-to-understand real-time summaries and highlight commentary for games, helping new fans quickly grasp what's happening on the field. Computer vision technology can automatically cut highlight clips from massive volumes of game footage—like the sliding scenes in MLB's playoff hype video—capturing attention in the shortest possible time. These capabilities were difficult to scale in the era of manual editing, but today they can be produced in real time and at scale.

Dynamic Pricing refers to a revenue management strategy that dynamically adjusts ticket or merchandise prices based on real-time supply and demand, game importance, team performance, weather, remaining seat locations and other factors. In professional sports, surging demand for popular playoff games drives up ticket prices, while less popular games can attract attendees through discounts. AI models can integrate signals such as historical sales data, social media buzz and competitors' schedules to predict demand curves and adjust prices automatically, thereby maximizing revenue while optimizing attendance. For MLB, dynamic pricing can also be combined with personalized recommendations—pushing ticketing options within each fan's price-sensitive range, lowering the barrier to a first game for new fans.

Natural Language Generation (NLG) is a branch of natural language processing that enables machines to automatically convert structured data into readable natural language text. In sports contexts, NLG systems can read structured data such as scores, player statistics and game event streams (e.g., hits, strikeouts, pitching changes) to automatically generate game recaps, real-time commentary summaries and player highlight descriptions. Unlike general-purpose large models, sports NLG typically combines templates with deep learning to ensure data accuracy and terminology standards. Its core value lies in scale—an MLB season has more than 2,400 regular-season games, and manually writing a summary for each is extremely costly, whereas NLG can produce them in real time and at scale, using plain language to lower the comprehension barrier for new fans.

The Fan Growth Dilemma and the League's Response

MLB faces a structural challenge: its core fan base is aging, and younger generations' attention is fragmented across more entertainment options. Finding new fans is essentially about finding a new generation of viewers. Social media videos are the entry point for reaching young people, but real conversion requires a more systematic strategy.

The league's approach can be summarized in a few directions: first, short, punchy content formats that fit the consumption habits of platforms like TikTok; second, turning star players into IP, leveraging individual star power to drive attention to the league; third, data storytelling, using AI-generated statistical insights to tell compelling game stories. None of these directions can succeed without technical infrastructure.

Implications for the Tech Industry

MLB's exploration offers a case study for observing AI's application in sports marketing. Sports leagues possess massive game data and user behavior data, making them a natural setting for AI deployment. But the challenges are equally obvious: how to balance personalized recommendations with privacy protection, how to prevent algorithmic recommendations from creating filter bubbles that lead fans to follow only a single team, and how to measure the relationship between social media exposure and actual fan conversion—none of these questions has a standard answer yet.

For content platforms and technology service providers, professional sports leagues are highly valuable enterprise customers. They have budgets, clear growth demands and rich data assets. AI-driven fan growth solutions could become an emerging track in sports technology. MLB's efforts to find new fans may be just the beginning of this transformation.

Filter Bubble, a term coined by scholar Eli Pariser, refers to how personalized recommendation algorithms continuously reinforce users' existing preferences based on their historical behavior, causing the information they encounter to become increasingly narrow and their horizons to shrink. In sports marketing, this risk manifests as follows: algorithms may only recommend content about teams a fan already follows, leaving them uninterested in other teams and the league's overall narrative—which actually works against the broad goal of "finding new fans." For a league like MLB with 30 teams, overly personalized recommendations could intensify fan fragmentation and weaken brand cohesion at the league level. Therefore, platforms and leagues need to proactively inject diversity into recommendation strategies—for example, interspersing cross-team highlights or league-level storylines within personalized feeds.

One-line Summary

MLB uses AI and social media to find new fans during the playoff window, reflecting sports leagues' growth anxiety and technological pivot.

Paragraph Summary

As the playoffs begin, MLB is posting hype videos on Instagram and TikTok aimed at converting casual fans into core fans. The article notes that posting content solely through official accounts is no longer enough to break through, and the league is ramping up data analytics and AI to optimize content distribution by analyzing user interaction patterns. AI applications extend to personalized ticketing, dynamic pricing, real-time data visualization and AR viewing, and can especially lower the cognitive barrier posed by baseball's complex rules through automatic summaries and highlight clips. Facing structural challenges of an aging core fan base and fragmented attention among younger audiences, MLB is responding with short-form content, star-player IP and data storytelling. This exploration also offers a case study for the sports tech sector, though questions around privacy balance, filter bubbles and conversion measurement remain unanswered.