A Primer To Using Data Science In Sports

How fit an athlete is? How is his or her match performance? How about the performance of an athlete in a particular session? Which player to buy in an auction? These are the few queries that would be significant for an athlete, a team, or team management.

Sports Analytics is an umbrella term that comprises an analysis of an athlete, a team, or team management through various forms of data. However, the data-crunching in sports could be categorized in two ways; sports statistics and fitness statistics (grossly). Sports statistics is could be simplified in the following ways:


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  • A Player Statistics: A player statistics states that an athlete performance throughout his or her career, or event/match day performance or in a session.
  • A Team Statistics: Overall analysis of team based on performance in a match or throughout the session.

As a sports analyst, it is too important to analyze the above-mentioned points during the in-session time; these analyses could also be stated as on field analysis. In, sports statistics; the data are in the form of numerical data, categorical data, and image type data.

Fitness statistics state all the data concerning the fitness attributes of an individual athlete that could cause an impact on the performance. For example; to test the cardiovascular endurance of an athlete Yo-Yo test is conducted, to test agility T-test is conducted, however, these are few examples of the field-based test.

A large spectrum of laboratory-based tests is also conducted in various sports science centres like ISOKINETIC DYNOMOMETER, FORCE PLATE, ELECTROMYOGRAPHY, etc. that provide too much amount of data.


  1. Descriptive Statistics: The analysis of session statistics of a team or an athlete through the mean and five-point summary.
  2. Paired T-test, Independent T-test, and ANOVA: The analysis of two (t-test) or more (ANOVA) group could be done; suppose if an analyst has to examine the significant difference between two or more groups regarding any sort of training as demanded by management or coach.
  3. Relationship Establishment through correlation matrix between two fitness tests or any other attributes.  
Sports analytics companies in India are changing the face of game with data-led solutions


The application of supervised and unsupervised machine learning algorithm to benefit the performance of an individual athlete and a team is:

  1. Clustering: A grouping of players could be done to determine the high performing athlete in the same tier and low performing athletes in the other tier. Suppose if you have given a data set of one session and if you’ve to figure it out or moreover team management would be asked you to determine a high performing athlete in one group others in different to determine the budget of next session.

The coach of a team has asked to categorize the athlete based on the fitness; they have given fitness test data conducted by them. Thus, this analysis will help to coach to determine fitness level and make the Strength and conditioning program for a particular group depending upon the cluster.

  1. Regression:

To predict whether the team will win the match or not; whether an athlete will score the goal or not; whether an athlete is fit or not. Classification regression techniques like Decision trees, Random Forest, Logistic Regression, Support Vector Machine, etc. could be used to predict the outcome of binary class as above mentioned.

It would be quite interesting if we knew about which all fitness parameters suit to play that particular sport and predict the child that they should opt for that sport. So, to help sports management to classify an athlete or a child to choose particular sports depending upon a fitness parameter and not by liking.

An approach of regression technique particularly in cricket is to predict how much a team will be going to score in a match or how much a batsman will score. To predict the runs Linear Regression technique will be used.

  1. Principal Component Analysis: The analysis of sports could results in the extraction of too many features such as in football it would be subdivided into human physiology, biomechanics, fitness, and techniques analysis. The resultant features would be kinetics and kinematics (in various phases), fitness parameters (speed, agility, power, endurance, strength), etc. that may account for more than 30 parameters. Principal Component Analysis is a technique for simplifying a dataset by reducing the number of dimensions of multidimensional datasets to fewer than the original representation.
  2. Performance Impact: To evaluate the impact of an athlete in a match or during event among participants with the implication of Machine Learning algorithm. 
  3. Time Series Analysis: To evaluate the training load of an athlete and forecast the same.


Video analytics is widely used in the sports and fitness domain to analyze posture and motion analysis to figure out the asymmetries present in an individual body. With the implication of deep learning algorithms like Convolutional Neural Networks (CNNs) various models could be built and help in better understanding of deviation in posture and technique of an athlete.

  Thus, data science implications could be beneficial for an individual athlete to a team and at last nation by fetching medals in various events.

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Swetank Pathak
I am a Sports Data Scientist with extensive knowledge of sports science aims to provide betterment of sports teams and individual players in a data-driven approach with a prime focus on athlete performance and injury management and with experience executing data-driven solutions to increase efficiency, accuracy, and utility of internal data processing. Currently pursuing Post Graduate Program in Data Science and Business Analytics at The University of TEXAS at AUSTIN, McCombs School of Business & Great Lakes Institute of management.

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