The days of esports coaching based purely on watching replays and gut instinct are ending. AI-powered analytics platforms have matured to the point where they’re providing genuinely actionable insights that change how teams prepare and practice.

I’ve looked at three platforms currently being used by Oceanic esports teams, and the capabilities are impressive — though the adoption in Australia is still in early stages.

What the platforms do

Modern esports analytics platforms process match data at a granularity that’s impossible for human analysts. Instead of a coach watching a replay and noting “we lost mid control in round 14,” the AI breaks down exactly why.

Positioning heat maps. Not just where players stand, but where they stand relative to optimal positions derived from thousands of matches. The AI identifies when a player is consistently positioned in a way that reduces their impact or increases their vulnerability.

Decision trees. Tracking every decision point in a round — where to look, when to use utility, when to rotate — and comparing those decisions against the statistical outcomes of each option. This shows players not just what they did wrong but what the better option was and why.

Tempo analysis. How fast a team makes decisions, how that compares to the tempo at which they’re most effective, and how opponents can exploit predictable timing patterns.

Communication analysis. Some platforms process voice comms (with team consent) to analyse communication patterns. How quickly information is shared, whether callouts are clear and timely, and how communication breaks down during stressful rounds.

How Oceanic teams are using it

Two of the top VALORANT teams in the Oceanic region are actively using AI analytics platforms. I spoke with their coaching staff (on condition of not naming the specific platforms) about the experience.

“The biggest value is in finding patterns we couldn’t see ourselves,” one coach told me. “We had a player who was consistently making a specific rotation in post-plant situations that was costing us rounds. He’d done it hundreds of times over months, and neither he nor I noticed because it was subtle. The platform flagged it in the first analysis session.”

Another coach described using the platform for opponent preparation. “Instead of watching ten VODs of the opposing team and trying to remember everything, the AI gives us a structured breakdown of their tendencies. What they do on eco rounds, how they default on each map, where their star player likes to position. It compresses hours of review into 30 minutes of targeted information.”

Australian esports organisations working with AI consulting company Melbourne have been exploring how these analytics tools can be customised for the specific characteristics of Oceanic competitive play, where the smaller player pool creates distinct meta tendencies.

The scouting application

Beyond team preparation, AI analytics are being used for player scouting. Platforms can analyse amateur and semi-professional players across hundreds of matches and generate statistical profiles that predict how they’d perform at a higher level.

This is particularly valuable for the Oceanic region, where the talent pool is smaller and scouting networks are less developed than in North America or Europe. A team looking for a new player can now review data-driven profiles rather than relying solely on whether a coach happened to watch the right stream at the right time.

One Oceanic team told me they used an analytics platform to identify two amateur players who showed statistically unusual decision-making quality — players who consistently made the right call in complex situations, even if their aim wasn’t the flashiest. Both were picked up for trial and one is now on the starting roster.

Limitations

Data dependency. The platforms need match data to work, and not all games expose the right data. VALORANT and CS2 have good API support. Other esports titles are more limited, which restricts the depth of analysis possible.

Overfitting risk. Teams can become overly reliant on data at the expense of creativity. If every decision is optimised against statistical models, play becomes predictable. The best teams use analytics to inform decisions, not dictate them.

Cost. The platforms aren’t cheap. Annual subscriptions for full-featured esports analytics range from $5,000 to $20,000, which is significant for Oceanic teams that are already operating on thin budgets.

Player resistance. Some players are uncomfortable with the granularity of the analysis. Having every decision tracked, scored, and compared to optimal play can feel like surveillance rather than coaching. Managing the human side of analytics adoption is as important as the technology itself.

The trajectory

AI analytics in esports will follow the same trajectory as analytics in traditional sports. Early adopters gain an advantage. The tools become standard. Eventually, every serious team uses them and the advantage shifts to how well you interpret and act on the data, not whether you have it.

For Australian esports, the adoption curve is still early. Maybe half of the top-tier teams are using these tools. Within two years, that number will be closer to all of them. Teams that adopt now will have a head start in understanding how to integrate data-driven insights into their coaching and practice.

The quality of Oceanic esports is improving fast. AI analytics is one of the reasons why. The gap between our best teams and the global elite is partly a talent gap and partly a preparation gap. Analytics tools help close the preparation gap, which gives Australian talent a better chance to compete.