Esports Talent Identification

Proprietary technology that identifies esports player talent potential with 86% probability.

Using this method, in 2020 we recommended x-kom AGO players Milosz 'mhl' Knasiak and Maciek 'f1ku' Miklas, who within a year became the leading figures of their generation.

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In 2020, ELAB launched a study to answer the question: What sets pro gamers apart from gamers with similar gaming experiences?

So far we surveyed over 200 participants - pro and semi-pro CS:GO and LoL players. Each gamer provided data from cognitive and physical domains as well as psychological data on mental health, personality, and temperament. The factors in this unique data set allow us to identify individuals with talent for professional e-sports with an 86% probability.

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The Esports Talent Identification, is the first ever method for identifying esports talent based on interdisciplinary research.

We have developed methods to capture unique datasets on mental performance, physical and mental health of professional and semi-professional CS:GO and LoL players achieving 86% accuracy in identifying esports talent.

Talent identification and development is one of the most important aspects of any professional sport. Currently, esports mainly relies on observation by coaches and scouts as tools to find players with exceptional potential. This method has many imperfections, but the biggest one is the limited number of experts with the right qualifications. ELAB aims to support this process with additional layers of data, shedding light on previously overlooked or unavailable insights.

To achieve this goal, in 2020 we launched the largest and most comprehensive scientific endeavor in esports to understand what separates professional esports athletes from players with similar experience?

In designing this study, we built on six years of academic research on how cognitive ability is linked to performance in esports. Working with academic experts in the key factors influencing human performance in sport, we expanded our approach to create an interdisciplinary "Esports Talent Identification". This is the first such comprehensive approach to identifying talent in esports. Our model can be divided into four main factors:

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Cognitive Abilities

We selected highly precise measurement tools based on previous research experience and benchmarks for cognitive ability tests. We measure perceptual ability, inhibitory control, cognitive flexibility, and processing speed, among others.

Physical Health

In collaboration with experts, we have developed a multifactorial approach to measure esports physical health. Our procedure consists of a physical fitness assessment (measuring muscle strength, upper body strength and body mass index).

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Mental health

Analytical understanding of who players are, in terms of personality traits or temperament, is important not only for the personal development of players, but also for better understanding within a team. Assessing the quality of mental health reveals trends in an individual's behavior that may impact the quality of a player's performance.

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Soft skills

An individual qualitative interview on soft mental skills, important from a professional team sport perspective, provides a picture of the player's deficits in managing mental health quality.

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Research

Apr 21, 2021

Does caffeine have a positive effect on esports players’ hit accuracy and reaction time?

Caffeine is an everyday part of many peoples’ lives. We start the day with a cup of coffee to concentrate faster and wake up after a few hours of sleep. In the case of esports players, it’s often energy drinks that are being consumed before an important match or long day of training with the team. No one knew though if the caffeine is affecting players’ accuracy and reaction time. In April 2020 someone finally decided to test it.

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Business

Are you an owner of an esports team or maybe want to develop your division? Feel free to contact us!

business@esportslab.gg

General questions

Any other questions? Don’t hesitate to contact us!

contact@esportslab.gg