• Sports Med Analytics

The Growing Use Of Analytics In Sports Medicine

In sports medicine and orthopedics, analytic techniques are utilized on a growing basis. Athletes and teams collect data in numerous ways.

The methods comprise wearable gadgets, intelligent clothing, and others, encompassing universal positioning and electromechanical devices, heart rhythm, and sleep info.

The Evidence

There is growing evidence to indicate that by recognizing potential risks for trauma in sports, these findings may help to guide appropriate preventive initiatives.

With software kits, researchers can analyze biometric data from these devices with add-on functionality to analyze basic metrics, such as electromechanical devices.

As several parties may use data with interests that may agree or disagree with the athlete's interests, ethical concerns about data collection, storage, and use have yet to be established.

It is increasingly expected that health care providers will view and use data that provide patients with information regarding results and activity levels.

For the use of devices to reliably predict sports-related accidents, there were no explicit algorithms, to our knowledge. Invalid algorithms can trigger training changes that can eventually lead to injury, so careful use of such devices is required.

Use of Artificial Intelligence

The use of artificial intelligence (AI) unlocks a fascinating viewpoint in competitive sports to anticipate injury risk and results. Better awareness of the AI approaches used and the sports that use AI is warranted.

The purpose of this research is to define which AI methods have been used to examine the performance of sports and the risk of injury and to find out which each sport has used AI strategies.


The Researchers did structured surveys for papers documenting AI strategies and tactics introduced to team sports players via PubMed and other scholarly internet sources.


The study involved fifty-eight experiments of nearly a dozen AI techniques or approaches being implemented in twelve competitive sports.

The combined sample comprised almost 6500 young participants (majority males), of whom 75 percent were skilled sports players.

Artificial neural networks(ANN), Decision Tree Classifier(DTC), and vector devices, and Markov procedure were the AI methodologies most commonly used with strong performance measures for all of them.

The organized sports, with many more AI technologies, were volley, basket and handball, and soccer.


Determined by the number of published reports, this study's findings indicate a widespread application of Ai techniques in competitive sports.

The present state of growth indicates a prosperous future concerning AI use in team sports.

In order to determine the predictive performance of individual AI approaches and practices, more assessment research based on potential strategies is required.

Core Factors

ANN, DTC, Markov technique, and support vector machines are AI techniques or methods currently used for sporting performance prediction in sports such as basketball, soccer, and volleyball.

The above techniques have been used in Australian and American soccer, football and handball for injury risk assessment.

Because of the ongoing advancement of the field and the introduction of assessment analysis in sports practice, the utilization of analytical techniques in team sports can transform further to assess the predictive success of each particular method.

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