Most athletes now use mobile apps to log their performance data, making analytics simpler. Big data has taken tracking further, with tools like IBM's SlamTracker monitoring real-time tennis stats. Sports analytics has become highly personalized down to individual players, with stats tracking narrow trajectories, yearly comparisons, and categories in sports like football.

1. Big Data's impact on sports
performance

2. Democratized analytics
Most athletes utilize some type of mobile app or similar technology to enhance or monitor
their performance. For example, there are a variety of apps that track and sort data for distance
runners and cyclists focused on logging training runs and rides. Many of these apps have
democratized the concept of data analytics, implementing easily accessible entry interfaces,
deep storage capabilities, and even sharing opportunities rooted in social media (Strava, for
example) to connect athletes with one another based on exchanged data.
These apps have made data analytics much simpler for athletes concerned with keeping
performance data for future reference.

3. Monitoring
performance
Big data’s tracking of performance stats,
however, goes far beyond that offered by simple
logging technologies. Take IBM’s SlamTracker
tool, which has been used during tennis
tournaments to track “real-time scores, statistics,
and point-by-point analysis of a match.”
Other prominent examples of data monitoring
can be found in basketball, American football,
soccer, and baseball. Statistical information
pertaining to a single NFL player, for instance,
has become incredibly narrow in its trajectories,
year-by-year comparisons, and most importantly
its vast array of statistical categories.

4. Transcending personalization
Big data’s tracking of performance stats, however, goes far beyond that offered by simple logging
technologies. Take IBM’s SlamTracker tool, which has been used during tennis tournaments to track “real-
time scores, statistics, and point-by-point analysis of a match.”
Other prominent examples of data monitoring can be found in basketball, American football, soccer, and
baseball. Statistical information pertaining to a single NFL player, for instance, has become incredibly
narrow in its trajectories, year-by-year comparisons, and most importantly its vast array of statistical
categories.