A Data-Driven Analysis of
Fan Engagement in USL League One Soccer

Soccer isn’t just the most popular sport in the world, it’s a multi-billion euro industry with profound economic impact. As the biggest soccer league in the world, the Premier League is projected to generate nearly 7.5 billion euros in the 2024/25 season. As with any entertainment business, digital presence is key to success. According to Sports Business Journal, during the 2022 World Cup, 811 million social media accounts engaged with FIFA channels – a record for any global sporting event. 
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While soccer has historically been most prevalent in European countries, it has just recently become notably popular in the United States. This year’s United States of Soccer Fan Insights Report found the number of first-time fans is up 400% year-over-year in the US. This recent spike in interest can be attributed to the 2026 FIFA World Cup, the first in the US since 1994. Much of this sport’s popularity in America is driven by a young, diverse and digitally-connected fanbase.

To understand how social media activity can drive real business outcomes, Manhattan Strategies conducted an analysis of existing USL League One teams and those set to join in 2025. With a global level of visibility through digital platforms, soccer teams are connecting with potential fans outside of in-game environments and drawing them into the ticket booth. Our research used data-driven insights of social media following and attendance to determine the degree of correlation between the two.

To develop a foundational understanding of the social media landscape of USL League One teams, each team must be put in the context of their own digital presence as it relates to the broader success of their business. Currently USL-1 has 12 total teams that are established within the league from 2019-2023. Due to the longer history of these teams, we can analyze their social media presence and attendance to review any statistical significant traits.

To visually represent the relationship between a team’s social media following and their in person attendance, a Lorenz Curve (and the Gini Coefficient derived from it) will be used to quantify the inequality of digital resources in the ‘population’ of all 12 USL-1 teams.

Figure 1 illustrates the ideal even distribution of followers as the gray dashed line, and the blue line represents the actual data set as of 10/05/2024.

The key data point in this chart, the Gini Coefficient, measures social media following in this way: In a perfect world, each soccer team would have the same number of social media followers. This would be represented by a Gini Coefficient of 0. On the other hand, a coefficient of 1 would indicate one team has all of the possible social media followers.

The above coefficient of 0.21 is very good, indicating a relatively even distribution of followers across the league. The next step – comparing this balance to the actual game attendance for each of the 12 teams.

With a comprehensive understanding of the social media landscape for the studied soccer teams, the next step is finding the digital audience’s relationship with the ticket booth. By performing the same data analysis for in-game attendance, we can find a potential correlation between these audiences.

The following data is each USL-1 team’s self-reported attendance numbers for FY23.  Here we can see a Gini Coefficient of 0.31, indicating a slight decrease in equality of distribution. This increased differentiation in Figure 2 suggests that there is an imperfect relationship between social media followers and game attendance, but a correlation nonetheless.

Clearly, this is not an exact 1:1 ratio, but we can see that social media following is indicative of in-game attendance.

As for the difference between the two Gini Coefficients, one possible hypothesis is that smaller inequality on digital is further exaggerated when it comes to the ticket booth. Whether one causes the other is unclear, but this evidence suggests that a cohesive social media campaign is crucial to ticket sales.

To dive deeper into the importance of a strong digital presence, Manhattan Strategies looked at correlation between social media following and in-person attendance broken down by each digital platform. The above Lorenze curves paint a picture of both social media and attendance for the 12 teams. To juxtapose these two data sets, a Pearson Correlation Matrix was created below.

The matrix above illustrates the correlation factor across Facebook, Twitter and Instagram with aggregate attendance. As we can see, attendance is most closely correlated with Twitter following – meaning that we can expect the number of Twitter followers to be indicative of attendance.

One additional observation is the correlation between Instagram and Twitter. At a 0.79 this would classify as a very high correlation factor, suggesting that increased engagement in both platforms would be greatly efficient in translating to sales at the box office.

It is difficult to imply a causal relationship between social media, followers and attendance for many of these teams due to a variety of individual unknown factors (location, team success, players, etc), but we can still see a notable relationship between the two.

As the data above demonstrate, social media following on specific channels has a strong correlation to ticket sales. While this study was isolated to USL League One, the sports industry shares many commonalities and these insights are valuable across the entertainment sector.

To guide teams in maximizing their online engagement and driving ticket sales, our strategic recommendations include leveraging targeted influencer partnerships, focusing on engaging content across key social media platforms, and maintaining consistent audience interaction. As a NYC-based agency, our research was tailored to Instagram Influencers in the tri-state area to simulate geographic targeting, as any sports team would.

With this research, Manhattan Strategies unlocked a comprehensive understanding of the competitive sports landscape while applying a data-driven methodology to analysis of both USL-1 teams and prospective influencers for this market.