Data is the new gold. Businesses, politics, and basically the whole world rely on it nowadays. In gaming, where everything is very fast-paced, especially esports, analysts find themselves having to rely on multiple data to interpret live games.
This poses several questions, like how this data is collected, which data to take into consideration, and what the use of such data is.
The Role of Technology in Gaming and iGaming
The gaming industry is technology-based. So, when new technologies such as AI or machine learning are introduced, they’ll obviously be used. Some view artificial intelligence as a threat, but it’s too powerful to be ignored. When trained on a specific game, it can pull real-time data and provide insights and analyses as the match is happening.
However, AI isn’t sentient and lacks many features of human intelligence. When compared to humans, its intelligence is, in fact, quite limited, and it excels at what it is designed to do. It can’t outthink its own programming, which humans designed. Still, when trained right and given the correct instructions, it can act faster than a human. It’s an enormous advantage for analysts and their teams.
Measuring Player Performance
In esports, player performance is a determining factor. Analysts must consider several in-game metrics automatically recorded by the game. These metrics can vary depending on the game but will often include the kill-death ratio (or victory-losses for fighting games or sports games), accuracy, speed of execution of specific actions, and more.
It serves several purposes. For the team’s analyst, it’s used to develop new strategies and help the player improve. With a mix of watching the game and collecting this data, the analyst can then make recommendations and track the progress. Afterwards, it’ll be used to create training programs addressing shortcomings and improving on the player’s strengths.
When a metric decreases, the analyst can quickly react, and his job will then be to create specific drills to correct the negative course. That’s how pro players can reach their peak and improve over time.
Opponent Scouting
The other way data can be used is when an analyst external to the team needs to prepare for an event. For example, in boxing, the coach can provide recordings of the opponent’s previous fights, and in esports, the team’s history can be used. Analysts will try to find out the other teams’ strategies and player performances and to identify patterns the players can use to their advantage.
For example, in a game like Valorant, the analyst will look at each player’s preferred heroes since each one has a specialty and corresponds to a specific play style. Like in any game balanced for competition, there will be limitations to the character that other heroes will be best to exploit and counter.
The data must also include how the player is using the character to develop a unique strategy. Data analysis drives accuracy, and it’s the kind of preparation and knowledge that can change the outcome of a game, especially when the stakes are high.
In-Game Strategy Development
At a pro level, there are always patterns because the player needs them, consciously or not. This is especially true in team-based games, where members need to coordinate their actions. Analysts look for these patterns in their strategy development. It then involves crafting scenarios in which the team must react according to given instructions.
For example, in a team-based game like Counter-Strike, if the opposite team is known to place a sniper in an area near the bomb, a team member will have to take him out before another can defuse the bomb (or activate it).
Enhancing Viewer Experience
Some games offer frantic action, and it can be hard for the viewer to follow. The data collected can also be used by commentators.
They’ll use insights and statistics to describe what’s happening and what viewers can miss. It helps make broadcasts more engaging for newcomers, while entertaining experienced fans.
An effort is always made to present data in an accessible way. It allows viewers to understand the most complex aspects of an esports event. In other words, it’s educational, and data is central to spreading the discipline to a broader audience.
Data Collection Will Only Go Further in Esports
Technological improvements will only improve data collection, which is a sign of a bright future for esports. The integration of more accurate data with new tools will make the experience better for viewers. It’ll also help create more skilled players. For example, integrating biometric data. It could help analysts by providing insights into players’ health and performance.
Esports is dynamic, and data has become an essential component to the best teams’ success. Monitoring performance, scouting opponents, and developing strategies around these elements are all data-driven. However, it’s not something that’ll replace humans. It’s tools at the disposal of experts to deliver even better solutions to issues that get more complex as players evolve.
