Sports Analytics Redefining the Sports Industry
Every age imprints its cultural and technological vogue on its art and sports. In the ancient days arts and sports has evolved as a recreational pass time and now it has transformed into one of the major economic force of the society. Just over a decade following the development of Data Science, the arena of sports and arts underwent a radical change in terms of setting strategic plans and decision making. Thus, the onslaught of Data Analytics, machine learning and AI has brought about a revolutionary transformation in the Sports and Arts industry.
Sports Analytics adding value to stakeholders
Nowadays, irrespective of the nature of sporting events all the forms of sports and games could be connected to analytical possibilities that give the stake holders critical observation and better insights to have an optimal utilization of resources for the best deal. In this context, the role a sport analyst has become vital to any professional teams for deciding strategies and plan of actions. The sport analyst makes use of the historical data related to the players and teams which is collected from multiple sources and apply appropriate algorithms and models to draw insights. These observations help the stake holders like coach, team managers, players to plan the best possible tactics and approaches in the games.
Types of sports data
The data related to sports and games could be from multiple dimensions like the individual performance of the players, the reaction time of the players in different game situation, about the recent record of the team performance, about the environmental conditions during the game, about the mob support and reaction during the game, about the count of fair and foul play during the game, decision of referee regulating the matches etc. These data can be also categorized as on field data and off field data related to the team or player. The on-field data includes the details of performance of the players or team, the strategies and approach they take in the game etc., while the off-field data relates to the fan’s behavior, social media interaction, environmental conditions etc.
Types of Sports Analytics
The sports analytics add a high value to the stakeholders to position their activities in the right track. The descriptive sport analysis projects the exact performance of the player and team and reflects how the entire performance is positioned and distributed. Also it helps to understand how the performance of the players and team is skewed. Diagnostic sport analytics will help to identify the key strategies and actions that led to the current situation. This type of analytics helps the stakeholders to spot out and rectify the current bad practices that led to the poor performance or to sustain the best practices that helped the players or team to excel its best.
Predictive Analytics the key for decision making
Predictive sports analytics is highly significant in sport industry. It helps to evaluate the critical observations in a particular scenario and inform the team about the possibility of winning the game. These analytics help to identify the best strategy to be adopted in a particular scenario so that the probability of winning the game will be highest. Hence Predictive analytics add much value to the stakeholders to develop an action plan against the opponent teams on each game day to yield the best possible results in the event. The websites like ESPN, Sports Star, Cricbuzz etc use data science techniques to predict the performance of the players and teams in various league match to add value to the sport news. The international teams and premium clubs globally use predictive analysis techniques to predict the performance of the players and to fix a value to the players in the auction for building up a balanced team capable of winning the league matches.
Machine Learning and Sports Analytics
Sports Analytics use a variety of classical supervised machine learning models and unsupervised machine learning models for predicting and classifying the performance of the team and players. The use of neural network architecture and AI is now widely used in sports analytics for strategy recommendation, skill mapping and process selection. The computer vision and deep learning algorithms could be effectively used in automated umpiring of the match. This could be made possible with the visuals received from live streaming of images from multiple angles during the tournament.
Sentimental Analysis in Sports
In a broader spectrum sports analytics is done in three perspectives namely player analysis, team analysis and Fans management analysis. The first two perspectives of analysis focus on game winning approach of the analytical pipeline. While the fans management analysis does not directly contribute to the on field data but it caters more onto the sentimental analysis of the audience towards the team and players. In this current scenario where sports industry is viewed as a major economic force of the society, the popularity of the team or players and the money value it contributes to various league competitions is a key matter of concern. This knowledge is essential to position the tournament and promote the matches to the potential sport lovers.
Sports Analytics as an academic curriculum
Sports Academy and Colleges are incorporating the possibility of sports analytics in the curriculum. The process flow diagrams of various strategies and action plans related to game and data mapping at each phase could be made use of for creating appropriate models. These models could power up the game strategies relevant to the situations. Hence these approaches transform the skill-based strategies of games to data driven science-based strategies.
Therefore, without any doubt it could be comprehended that in this present-day society where the possibilities of data science span every walks of life, Sports analytics unfolds with immense opportunities to the job seekers to pursue a challenging career which is much dignified and valuable in the sports ecosystem.
