The sports world is currently generating vast data lakes of raw operational data that can become a game-changer for the industry when this big data is strategically harnessed and harvested. The application of advanced technology and methods like machine learning, AI, and data science enable sports organizations to move beyond mere report generation to an era of predicting outcomes in real-time, suggesting alternatives, and executing new ideas faster.
According to Grand View Research, the global market size of sports analytics was valued at USD 885 million in 2020 and is expected to increase at a growth rate (CAGR) of 21.3% from 2021 to 2028. Recent technological advancements in machine learning, AI, big data, and predictive analytics have triggered a paradigm shift in sports analytics. While big data helps drive team efficiency and increases revenue from various sources, machine learning algorithms and models provide predictions and recommendations to build a resilient and sound in-game strategy.
Machine learning in sports
Sports analytics applies supervised machine learning algorithms like naive bayes, decision trees, linear regression, and neural networks. Sports analytics also includes unsupervised machine learning algorithms such as k-means clustering and association rules. These algorithms act on sports data collected from various sources to draw valuable conclusions concerning player performance and team efficiency. Numerous scenarios exist where machine learning may be implemented in the sporting world.
Understanding player and team performance
Machine learning provides thorough data analysis that helps coaches and analysts understand factors contributing to a win or loss.
– Individual players’ performance over time as well as game-by-game
– Each players’ role in the outcome of a game
– On-field actions that contribute to the win/loss of the game
– Important player points, shots, and plays in specific situations
Prediction of injuries
Data science and AI-powered solutions help predict injuries and outcomes that may impact revenue generation, medical expenses, recovery, sponsorships, and ticket sales. One of the main reasons for injuries in the sporting arena is players’ excessive training sessions. Deep learning algorithms like convolutional neural networks (CNNs) and deep-CNNs identify and understand training impacts, player posture, and technique deviations. Logistic regression models help analyze how players react to any particular training stimulus and determine the potential risk of injury based on training workload, which then can be adjusted to minimize the risk of injuries.
Controlling random factors affecting performance
Apart from a players’ physical ability and skill in the game, other factors undoubtedly impact performance. These include field, weather conditions, players’ nutrition, sleep, team environment, and competitive factors. By applying machine learning to this type of data, analysts can determine a player’s actual and quantifiable physical ability, allowing the coaches, owners, and organizers to make the best possible training and team-building decisions.
Scouting of players
Machine learning techniques like clustering and statistical analysis make the process of searching for players much more efficient as these techniques provide a data-driven approach in identifying the right player for the proper position. Automated video analytics, along with positional and tracking data, help assess the players’ skills, biometrics, and medical information. These insights help the teams decide on the amount of money they should invest in players with a cost-benefit analysis, thus effectively distributing their resources to build the best team possible.
Player performance predictions based on past data
Machine learning predicts the players’ performances based on the historical data gathered on them. It allows you to determine the contract value of each player based on their objective performance rather than the subjective intuition and for better allocation of player development resources.
Understand audiences and their engagement
Understanding the sports audiences and their engagement in the sport is critical as happy and engaged audiences translate into higher game attendance and profits. Machine learning helps understand the fans and the trends in fanbase’s interests and behavior for better and targeted marketing campaigns, boost ticket sales and increase audience engagement. Churn prediction models with logistic regression help with understanding season ticket sales. Hypothesis testing using paired T-tests provides insights into the performance of targeted marketing campaigns.
Machine learning is a game-changer for the sports industry. The primary focus includes building machine-based models that help manage player fatigue, minimize injury, provide insights for pre-match & post-match analysis, personnel selection and mix, and coaching requirements. Empowered with these advanced insights, today’s modern sports franchise becomes more resilient and competitive with superior analytics and actionable intelligence delivered at just the right time.
If you want to gain a competitive advantage by leveraging machine learning in sports, send us a query to firstname.lastname@example.org. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect2TM. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellectdata.com.