How AI, Customization, and Better Event Design Can Change the Sports Fan Experience

Sports innovation works best when it does two things at once: it solves a real problem and makes the experience feel more alive for fans.

That standard matters now because sports organizations are surrounded by new tools, new data sources, new fan expectations, and new pressure to create events that feel worthy of attention. Artificial intelligence can help organizations move faster. Data can help decision makers see patterns more clearly. Creative scheduling can turn ordinary calendar moments into larger sports events. But none of those ideas matter if they are not rooted in the actual needs of fans, teams, partners, and communities.

The best innovations in sports are not simply the newest ideas. They are the ideas that connect technology, empathy, fan experience, and practical implementation.

That is why the future of sports innovation will belong to organizations that can combine technical capability with human understanding. AI, data, software, event formats, and rule changes should not replace the people who make sports meaningful. They should help those people make sports better.

The Core Problem or Opportunity

Sports organizations are often asked to innovate without a clear definition of what innovation should accomplish.

Some teams need better sponsorship tracking. Some leagues need cleaner data pipelines. Some event organizers need stronger economic impact measurement. Some fans want better access, more personalization, and more compelling reasons to pay attention. Some sports need fresh event formats that create a bigger sense of occasion.

The risk is that organizations chase technology before they understand the problem. A large software platform may offer dozens of features, but that does not mean it meets the specific needs of a league, team, sponsor, or local sports ecosystem. In many cases, organizations are forced to fit their work into tools built for someone else.

That is where the real opportunity sits: customization at scale.

When AI and software development are used well, sports organizations can build tools around actual workflows instead of forcing people into rigid systems. That shift can help smaller and mid sized sports organizations access technology that once required far more time, money, and technical capacity.

The same principle applies to event design. Major League Baseball has a unique product that most major sports do not have: the realistic ability to play day night doubleheaders across the league. Used strategically, that could become more than a scheduling quirk. It could become a full sport wide celebration.

Why This Matters Now

Sports are competing for attention in a crowded media environment. Fans can watch highlights, scroll through clips, follow athletes directly, check win probability charts, and move between games in real time. A normal game can still matter, but sports properties increasingly need moments that feel bigger than normal.

At the same time, AI has moved from an abstract concept into a daily work tool. Large language models can support brainstorming, software development, content creation, and workflow design. Machine learning already shapes parts of the fan experience, from win probability graphics to decision support for teams and broadcasts.

Yet the public conversation around AI often swings between hype and fear. One side promises that AI will solve everything. The other warns that AI will replace people. Both views can miss the more useful point.

AI is most valuable when it raises expectations for what people can build, personalize, and understand.

In sports, that means better tools for organizations, richer data for decision makers, more customized fan experiences, and new ways to create value around events. It does not mean eliminating human instinct. A win probability model can support analysis, but it cannot replace the judgment of a coach making a fourth down decision. A language model can accelerate software work, but it cannot replace the empathy required to understand what a client actually needs.

The same urgency exists for baseball. The MLB All Star Game remains a major event, but the sport has room to create a stronger second half kickoff. A league wide MLB Day built around day night doubleheaders could help baseball turn the return from the All Star break into a true fan experience.

What the Evidence Shows

Custom software works when it starts with empathy

One of the strongest examples from the Sports Innovation Institute at IU Indianapolis is sponsorship tracking software developed for the Horizon League.

The project started by identifying pain points. Traditional software often asks sports organizations to pay for capabilities they do not need, while failing to solve the problems they actually face. The better approach was to build around the partner’s specific needs.

AI helped change the timeline. A student with prior software development experience was able to produce in two months what normally might have taken years. That speed did not remove the need for strategy. It made the strategy more actionable.

The important lesson is that AI did not create value on its own. The value came from pairing AI with empathy, client knowledge, and a clear understanding of the workflow.

AI can support decisions without replacing decision makers

Sports fans already see AI assisted tools in familiar places. Win probability charts are one example. They help viewers understand the shifting stakes of a game and make wild finishes easier to visualize.

Teams and coaches may use data informed models to support decision making, but the strongest sports decisions still require interpretation. A model can estimate likely outcomes. A coach still has to understand personnel, momentum, pressure, health, context, and the emotional reality of competition.

That is the healthy role for AI in sports: decision support, not decision ownership.

Innovation can come from client work, not just theory

In academia, research can sometimes be separated from implementation. Sports innovation benefits when research, data analysis, and real world client work inform one another.

That shift matters because sports organizations do not need abstract insight alone. They need tools, systems, and strategies that can be tested in real environments. Client work can reveal the next research question. Research can then make the next solution stronger.

That cycle helps sport management move from observation to action.

MLB Day could turn a quiet calendar moment into a spectacle

The MLB Day concept is simple: after the All Star break, Major League Baseball could return with day night doubleheaders across the league. Thirty separate admissions, thirty games, and a full day of baseball could potentially create one of the sport’s biggest calendar moments.

The idea works because baseball has structural flexibility other sports do not. Doubleheaders are part of baseball’s history, and the All Star break gives players time off before the second half begins. A league wide day of doubleheaders could create an Opening Day type atmosphere in the middle of the season.

The fan experience could extend beyond the games themselves. Ballparks could host block parties between games. Broadcast partners could move from game to game all day. MLB could connect the event to Stand Up To Cancer or another charitable cause. The sport could create a tidal wave of baseball at a time when it needs to recapture attention after the break.

The main concern would be weather and possible rainouts. But that challenge does not erase the upside. Baseball already manages weather disruptions, and the event could be designed with flexible contingency plans.

Key Lessons or Strategic Takeaways

Innovation should start with the user’s actual problem.
Sports organizations should resist the temptation to buy or build technology because it sounds advanced. The best tools begin with pain points, workflows, and the people who will actually use them.

AI is a multiplier, not a replacement for judgment.
Machine learning, large language models, and data tools can accelerate work and expand what is possible. The practical value comes when people use those tools to make better decisions, not when they hand over responsibility.

Customization is becoming a competitive advantage.
Sports organizations no longer need to accept one size fits all systems as the only option. AI supported development can make custom solutions more realistic, especially for organizations that need focused tools rather than massive platforms.

Fan experience depends on spectacle and structure.
MLB Day is compelling because it uses something baseball already has, the doubleheader, and turns it into a coordinated league wide event. The best sports event formats often come from rethinking existing assets.

Human insight still drives the best sports innovation.
Technology can help build the system. Data can help reveal the pattern. But empathy, curiosity, and strategic judgment determine whether an idea truly improves the sport.

Practical Application

Sports leaders, event planners, and innovation teams can use a simple decision lens before adopting a new tool, format, or rule change.

Start with these questions:

• What problem are we actually solving?

• Who feels that problem most directly?

• Does this idea improve the fan experience, the operator experience, or both?

• Can technology make the solution faster, more customized, or more affordable?

• What human judgment still needs to remain central?

• What is the smallest useful version we can test?

• Does the idea create enough value to justify changing familiar routines?

For technology projects, the first step should be listening. Before selecting software or building a system, map the actual workflow. Identify where time is wasted, where data gets lost, where users feel friction, and where current tools force people into unnatural processes.

For event ideas, look for underused calendar moments. MLB Day is powerful because it does not require inventing a new sport. It reframes a moment that already exists: the return from the All Star break. Many sports properties have similar opportunities hiding in plain sight.

For AI use, separate brainstorming from authority. AI can help generate options, speed up code, draft structures, organize information, and support analysis. It should not be treated as the final expert in strategy, data interpretation, or ethical decision making.

Final Thoughts

Sports innovation is not about chasing novelty. It is about making the sport more useful, more engaging, more understandable, and more memorable.

The strongest ideas often combine a practical fix with a better fan experience. Custom sport software works when it is built around real needs. AI becomes valuable when it helps people create, analyze, and serve more effectively. MLB Day is compelling because it turns a familiar baseball tradition into a larger celebration.

The future of innovations in sports will not belong to organizations that simply adopt the most technology. It will belong to those that understand what technology should make possible.

About Dr. Liz Wanless

Dr. Liz Wanless is an associate professor of sport management at Indiana University Indianapolis and director of the Sport Innovation Institute. Her work focuses on sport innovation, applied data science, AI supported tools, software development, and strategic support for sport organizations in the Indianapolis sports ecosystem.

She is also a former national champion shot putter, a four time All American at Bates College, and a former Division I All American at the University of Florida.