In recent times, analytics has created a niche for itself in the sports industry at large. With increasing competitiveness, teams, athletes, and management across the board have realized the need to complement their training with machine learning and artificial intelligence driven video analytics. With the ability to host and analyze in cloud, the same can be leveraged to scale and achieve cost efficiency simultaneously.
This article demonstrates a framework to identify and use analytics on certain KPIs and metrics, which can help athletes understand and optimize their performance. The framework can be applied to a wide variety of sports and athletes. With the use of analytics, athletes will be able to measure themselves against their competition and find areas of improvement, if any. The framework can be customized to a particular sport and key metrics can be defined accordingly.
How it works
The framework emphasizes on three different areas i.e. recording the video during the event, followed by competition analysis, and finally training analysis. From a data collection point of view, both options of near real-time analysis as well as post-match analysis are available. From an analysis point of view, both competition as well as training analysis are provisioned. As far as competition analysis is concerned, let’s focus on post-match analysis like generating the performance profile for every athlete, creating a comparison among players on different parameters, and how this affects the overall result of the match. In training analysis, we can concentrate on checking the progress of athletes by creating custom drill sessions to work on athletes’ weaknesses to achieve optimal performance.
For starters, we need to start collecting data on the performance of athletes using video recordings that takes place during the live sporting event. The videos that are recorded can be uploaded into a video analytics application and our SaaS model will work on it. For this process of data collection, on the hardware front, we will need access to the video recording done by a professional cameraman, or we can use portable cameras. The cameras need to be calibrated appropriately in accurate angles to cover the movement of athletes on the field. The data collected will then be uploaded real-time, or after the match to the cloud, for processing via mobile application or website.
The next step involves analysis of the data captured. To begin with, we need to identify the athletes and use facial recognition. The analysis can be performed in a near real-time manner or after the event has taken place. The required key performance metrics will be extracted from the SaaS model. Goals and objectives can be set on this model, against which performance is measured. Coaches and athletes together can decide on desired goals according to their needs. These KPIs will vary with different sports. Some of the common metrics include overall average athlete speed, distance, and trajectory. Some of the sport-specific KPIs include number of passes made by a player, basket count, accuracy in case of football and basketball.
The final step is to provide analysis in the form of interactive dashboards that provide real-time feedback and help coordinate with the athlete about improving his/her activity. The dashboard will be available via the mobile app on the device. The athlete will be capable of continuous improvement via accurate tracking of progress. As depicted by the infographic below, there is a lot of scope for the coaching platform using video analytics in sports with analytics forecasts estimating a massive 33% CAGR and $17.07 B incremental growth in market size from 2019 to 2023.