Back To Top

Analytics in sports

-Sourya Poddar

Sports analytics is a collection of relevant, historical, statistics that when properly applied can provide a competitive advantage to a team or individual. Nowadays it’s not only tracking data and gaining actionable insights. Today real-time videos are used for the purpose of finding key analysis points. Using the player tracking software, one can rate players and understand his/her performance and scopes of improvement. Its help coaches to design the training to improve player performance. Opposition analysis is also a big aspect of sports analytics. Detecting an opposition’s playing pattern provides an advantage to the coach to build game tactics. Its all about staying ahead of the game.

Sports analytics are of two types:

  1. On-field analytics
    1. Improving the on-field performance of teams and players.
    2. Opposition analysis
    3. Game tactics and team line-up
    4. Monitor player fatigue level, fitness and reduce injury
    5. Determine training schedule
  2. Off-field analytics
    1. Understanding fan preferences and increasing fan engagement
    2. Increase ticket and merchandise sales
    3. Player recruitment
    4. Marketing strategy, acquire sponsors and increase profitability
    5. Sports gambling

Aspects of sports analytics:

  1. Data Management
  2. Data analysis and extracting meaningful insights from data
  3. Simplistic data visualization for quick understanding and easy decision making

Data Management:

Data management includes any and all processes associated with acquiring, verifying and storing data in an efficient manner. In a sports organization, data can come from a variety of sources and may be presented in many different forms, be it structured or unstructured data. Sources can be:

  1. Real time on-field data via wearable devices
  2. Post match structured data
  3. Video footage captured during matches and training sessions

Data Analysis:

It is the process of applying statistical tools to data to gain insight into what is likely to happen in the future. This can project the career of players, identify strengths-weaknesses of an team, or assessing the appropriate cost of a player. These analyses can range from simple comparisons to extremely complicated and cutting-edge statistical analysis.

Data Representation:

The results of these analyses may feed directly into an intelligent information system that provides decision-makers with standardized results or they may be reported directly to a decision-maker for special projects that may be outside of any standard systems. It’s important to present the result in a meaningful way that can be easily understandable by player or coaches.

The hurdles that come across in case of sports analytics are uncertainties in terms of player profiling or opposition that vary from discipline to discipline. Analyzing all the 22 players in a free-flowing sport often pose as a challenge, also, scores are the final criteria that matter the most in every sport, and  this can distort all other statistics. Finally, everything depends on how efficiently the players execute the plans on-field. However, all analysis fall flat against individual brilliance and skills of the players.

Speak to AI Expert