Data Collection in Sports Analytics
Gyumin Kim
In today’s fast-paced world, the growth of data is exponential, permeating into every industry and aspect of our lives. According to IDC’s Global DataSphere, which forecasts the annual growth of data volume, data will grow at an annual compound growth of 21.2% for the next 5 years. The sports industry is no exception, as it adapts to the surging speed of data and embraces the implementation of data science to enhance the numerous aspects of its industry. This includes data implementation in sports marketing, which aims to find the optimal marketing strategies to increase the fan base for the team. It can also include sports business analytics, where data analysis is done to predict which financial action will lead to greater profit for the sports organization. While these aspects are significant areas in the sports industry where the Big Data tool is used, we will delve into data implementation in a more fundamental outlook in the sports industry: Sports Analytics, as seen in Fig. 1.
Big Data analysis is mainly divided into five steps: identification, data collection, cleaning, analysis, and interpretation. In this article, we will focus on the second step, data collection. Data collection is defined as “the process of gathering massive amounts of data through a variety of sources”. Data collection is relatively overlooked compared to other processes in Big Data such as data analysis and data interpretation in Fig. 2. This is because useful patterns are finally seen from the eyes when the raw data points are converted into visual graphics through data analysis. However, one needs to realize that the patterns from the data analysis are only valid and useful when the data collection process is done thoroughly with high accuracy.
Through sports analytics, teams, and individual athletes have different goals they are eager to achieve. One of the goals can be to prevent future injuries. Other goals can be to find the optimal strategy to win or to create a workout plan for an athlete. And so, to achieve these different purposes, the data collection methods in sports analytics are differentiated. Below are the 4 most utilized data collection methods in sports analytics.
1. Wearable Technology
In the rising world of nanotechnology, fitting GPS and nano-sensors in a light suit isn’t a big problem. These wearable technologies are made light and comfortable so that data can be collected while the player is in actual games or during practice as shown in Fig. 3. The wearable devices track their movements, heart rate, and other physiological parameters. This data is collected and analyzed to monitor performance, optimize training programs, and prevent injuries.
2. Risk Assessment
Risk assessment collects data such as player’s performance, player’s physical status, and injury histories to find the risk factors and the patterns regarding injuries. By identifying the risk factors, the athlete can have a lower chance of injury by excluding risky training or strategies and creating a modified one instead.
3. Load Management
Load management involves sports performance software that can record and track player’s workloads such as training intensity, playing time, and the body recovery period. From the data collected, the players can prevent fatigue and overexertion by being able to calculate the optimal range of workload as shown in Fig. 4. Load management can also be utilized to create the most effective workout plan for the athlete based on the load management analysis.
4. Biomechanics Analysis
Using motion-capture technology and biomechanics modeling, the biomechanics analysis breaks down a player’s movement patterns and technique. The analysis can determine the faulty movement pattern that the athlete possesses which will likely lead to a higher chance of injury. It will help identify the wrong movement or technique the player has possessed and give recommended feedback on how the player’s movement can be improved.
Data collection is a fundamental component of sports analytics, enabling the subsequent steps, data analysis, and data interpretation in sports analytics to offer valuable insights into the sports game and the athlete. The utilization of data science has revolutionized the sports industry, empowering teams to optimize performance, enhance strategies, and engage fans on a whole new level. Through methods such as wearable technology, load management, risk assessment, and biomechanics analysis, sports organizations can gather comprehensive data sets that provide a deeper understanding of the game and prevent players’ injuries. As data collection in sports analytics continues to evolve, it holds immense potential to unlock new possibilities, shape future strategies, and create a more immersive sports experience for teams and fans alike.
References:
Data collection — What is it and why is it important? (2023, September 14). ATLAS.ti. https://atlasti.com/guides/qualitative-research-guide-part-1/data-collection
How sports analytics are used today, by teams and fans. (2022, January 11). Built In. https://builtin.com/big-data/big-data-companies-sports
Types of big data analytics and their advantages | FORE. (2022, May 2). FORE School Of Management. https://www.fsm.ac.in/blog/an-insight-into-big-data-analytics/
Why sports analytics prevent football injuries. (2023, May 26). KINEXON. https://kinexon.com/blog/sports-analytics-prevent-football-injuries/
