Across the landscape of elite competitive sports, athlete performance insights have become valuable currency. As performance-tracking technology and data-processing power have improved – including recent years’ quantum leaps – sports organizations are gaining access to greater volumes and depths of insights, which help inform best practices for athlete training, rehabilitation and recovery.
In the NBA, the gathering and analysis of those performance insights has taken on new urgency. In September, only weeks before the start of the 2023-24 NBA season, the league’s board of governors issued a player participation policy designed to push teams to strike a balance between responsible load-management practices and player availability, an issue that has increasingly tested the patience of fans.
With NBA leadership concerned about broadcast ratings, ticket sales and the overall fan experience, the player participation mandate was enacted to hold clubs accountable for showcasing the game’s biggest stars. At the same time, it will require teams’ performance staff to change their approach to load-management – from training methods to assessments to recommendations. Many NBA teams already excel at capturing and understanding high-level in-game data. Now those skills are critical to everyday on-court personnel management across the league.
Different Sports, Different Data Sets
As mentioned, balancing athlete health with availability isn’t a concern limited to the NBA. Load management is a constant concern throughout sports. Organizations of all kinds, often including even competitive amateur youth programs, engage in long travel and demanding schedules. Load management involves the continuous calibration of the varied aspects of athletes’ routines.
The collection of in-game performance data is an important component of load management, but without further context, the results of that data capture offer only a glimpse of a much larger, more complex picture. Some sports organizations play several games in a week (including NBA teams) and travel tens of thousands of miles over the course of a season. Others may be provided less-than-ideal accommodations – think minor league baseball clubs – which may affect an athlete’s sleep cycle, circulation and other performance-influencing factors.
Capturing performance data from games, training and rehabilitation sessions, and analyzing them within the context of an athlete’s travel schedule, practice routines and rehab programs are key to understanding and optimizing the elements that contribute to overall athletic performance. Given the current emphasis on performance in sports, and aided by advances in tracking technology and data analysis, it’s not only possible to delve into many of the deepest insights that once escaped performance practitioners – it is crucial.
Data-Driven Load Management: The Future of Performance
The application of advanced analytics and load-management practices is a relatively recent development in professional basketball, and some sports are still wrangling with the concept. Because different sports can harbor a number of unique factors that contribute to player fatigue, identifying and understanding these differences is the key to tailoring load-management strategies based on the specific demands of each sport.
The increasingly nuanced approach to load management is prompting more organizations to explore a wider range of data sources. This may include not only traditional statistics but also advanced metrics and technology-driven insights. Team practitioners may, for instance, consider factors such as an athlete’s force-velocity profile and fatigue metrics in order to optimize training and performance.
In the NBA and across sports, more organizational leaders are learning the importance of athlete load management, as well as the role that technology and data science play in that process. Metrics and insights derived from data are at the crux of the development and implementation of effective load-management strategies. Simply put, with deeper movement insights, teams equip themselves with the information needed to optimize both player availability and athlete performance.