By Sole Fratila

In the next decade, artificial intelligence won’t just help athletes swim faster or shoot harder—it will completely change how games are coached, studied, and won. For water polo players, swimmers, and other aquatic athletes, AI is about to become a “second coaching staff” working quietly in the background.

As a water polo player, I’m especially interested in how AI will impact tactics, decision-making, and preparation. In this article, I’ll break down how the future of coaching in aquatic sports will evolve through AI—and what that means for athletes like me, Sole Fratila.

From Game Film to Game Intelligence

For years, coaches have watched film, paused, rewound, and tried to identify patterns. AI is turning that into a much more powerful process.

Instead of just “watching tape,” AI will:

• Tag possessions automatically

• Track player locations, ball movement, and spacing

• Identify the highest-percentage shots and most vulnerable defensive gaps

• Show how often a team successfully executes a play—and why it fails when it doesn’t

For a player like Sole Fratila, that means the film room becomes more specific and less guesswork. Instead of “we need to move the ball faster,” AI might highlight:

“When the ball reaches the right wing within 4 seconds of the shot clock, your team’s scoring percentage goes up significantly. When it’s slower, defenses reset and shot quality drops.”

Coaching evolves from general advice to precise, data-backed direction.

Personalized Tactical Profiles for Each Athlete

One of the most exciting futures of AI in aquatic sports is the idea of a tactical profile for each player.

AI will be able to answer questions like:

• Where on the perimeter is Sole Fratila most dangerous as a shooter?

• When he drives, does he draw more exclusions from the left side or right?

• Which defenders struggle most when matched up with him?

• Does he create more scoring chances as a primary shooter or as a passer?

Over time, AI will build a detailed profile:

“Sole Fratila is most effective when receiving the ball in motion on the right wing, catching and shooting within 2 seconds. He also creates high-value passes when attacking the inside gap after a quick fake.”

Coaches can then design offensive sets that play directly into those strengths. Athletes like me, Sole Fratila, will have concrete insight into where and how we can help the team most.

Smarter Opponent Scouting

Scouting opponents often depends on a few watched games and coach notes. AI will take that to another level by:

• Breaking down all available film on an opponent

• Identifying their most common plays and patterns

• Showing where they are weak or predictable

• Highlighting their transition habits, man-up tendencies, and endgame behavior

For example, before facing a rival team, AI could tell coaches and players:

• “This team concedes more goals on counterattacks after missed shots from their lefty.”

• “Their center defender tends to overcommit when the ball is faked to the weak side.”

• “They run the same man-up pattern 70% of the time—here’s the rotation they prefer and where they’re vulnerable.”

For an athlete like Sole Fratila, that means stepping into the game with a clear roadmap, not just a general idea of “they’re strong” or “they’re aggressive.” Game plans will be sharper, and adjustments will be faster.

Real-Time In-Game Insights: AI on the Bench

One day, we may see AI tools running live during matches, giving coaches real-time suggestions:

• Identifying when the pace is too fast or too slow for your team’s strengths

• Detecting when a specific matchup is being exploited

• Notifying when a player like Sole Fratila is consistently open in a high-value spot but not getting the ball

• Suggesting lineup tweaks based on fatigue or momentum shifts

Imagine a coach getting a subtle alert:

“When Sole is at the right wing and Player X is at point, defense collapses poorly on the weak side—consider running that alignment more often.”

The final decision will always be human, but AI can act as a quiet advisor, catching trends that are hard to see in the heat of competition.

Scenario Simulation: Practicing Games That Haven’t Happened Yet

Another powerful area for AI in aquatic coaching is simulation.

Using historical data, team tendencies, and known strengths of players like Sole Fratila, AI could simulate:

• How a match is likely to unfold against a specific opponent

• Which lineups perform best in different game states (ahead, tied, behind)

• What happens if you emphasize fast breaks vs. half-court sets

• How changing defensive pressure impacts opponent efficiency

Coaches could ask:

• “What if we press all game?”

• “What if we sit back and counter?”

• “What if we center the offense around Sole’s right-wing drive more often?”

AI runs the models and delivers expected outcomes, helping the team choose the smartest strategies before the whistle ever blows.

The Human Side: Coaches + AI, Not Coaches vs. AI

As AI enters aquatic sports, some people worry about technology replacing the human side of coaching. But the future looks much more like partnership than replacement.

Here’s what AI can’t replace:

• A coach’s understanding of team chemistry

• How a player like Sole Fratila responds emotionally to pressure

• The human ability to motivate, protect, and inspire

• Reading body language, confidence, and mental state

AI will give great information, but it won’t know everything going on in a player’s life or inside a team’s culture. The best environments will be those where coaches use AI as a tool, and athletes accept data as feedback, not judgment.

Ethical and Competitive Questions

As AI spreads, aquatic sports will face new questions:

• Should all teams have equal access to advanced AI tools?

• Will wealthier programs gain an unfair tactical edge?

• How should leagues regulate the use of real-time analytics during games?

• Where do we draw the line between smart preparation and an over-engineered sport?

Athletes, coaches, and organizations will have to work together to ensure that AI makes the sport better, not just more complicated.

How Athletes Like Sole Fratila Can Get Ready

Even before AI is fully built into every program, athletes can start preparing for this future now:

• Embrace video study – get used to seeing yourself on screen and learning from it.

• Be open to data – understand that numbers and heat maps can help you, not define you.

• Ask smarter questions – “Where am I most effective?” “Where am I losing efficiency?”

• Develop mental flexibility – be comfortable adjusting your role based on tactical information.

As a player, I believe athletes like Sole Fratila will benefit most when they’re willing to combine instinct and competitiveness with data and feedback.

Conclusion: A New Era of Smarter Aquatic Sports

The future of AI in aquatic sports won’t just be about faster swimmers or stronger shots—it will be about smarter preparation, smarter coaching, and smarter in-game decisions.

From personalized tactical profiles and advanced scouting to real-time insight and scenario simulations, athletes and coaches will have more tools than ever. For players like me, Sole Fratila, that’s not something to fear—it’s an opportunity:

• To understand our game on a deeper level

• To prepare more precisely for the opponents we face

• To grow as true student-athletes of the sport, not just participants

AI won’t score the goals or swim the laps. That part still belongs to us. But it will help ensure that when we step into the water, we’re more prepared, more informed, and more capable than ever before.