Sports analytics

Big data has become the sports industry's greatest asset, from past data and basic scorekeeping to algorithmic performance prediction and particular player statistics. Sports companies use data analytics on and off the field to inspect their athletes’ performance, optimise operations and keep fans engaged.

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What are sports analytics?

Sports analytics use data analysis to improve athletic performance and efficiency. It involves collecting, processing and interpreting data to help with decision-making before, during and after games on and off the field. 

 

Sports analytics blend data science and athletic performance to gain a competitive advantage, from strategising game plans to refining individual skills. It's not just the athletes who benefit from sports analytics but the entire organisation.  

 

Coaches rely on sports analytics to scout opponents and optimise gameplay calls, while front offices use them to prioritise player development. Sports analytics also allows organisations to leverage data to monitor ticket sales, reduce operational costs and improve fan engagement. 

 

Sports analytics have also changed the way fans consume sports. Sports analysts, commentators and fans constantly use data to explain play-by-play, discuss predictions or power fantasy league decisions. Fans can browse websites or mobile apps for data-based sports coverage and their favourite team’s odds of winning a championship. 

 

Additionally, sports analytics have extended to betting companies, who rely on data to determine the odds of specific game results happening. Fans then consider these odds when placing bets, selecting players for a fantasy team and making other decisions that depend on statistical data. 

Sports data analytics

There are two types of sports analytics: on-field and off-field.  

On-field analytics

On-field analytics includes tracking metrics to impact approaches that may be used to enhance in-game tactics, nutrition planning and athletic performance. On-field data lets teams and organisations track performance, make predictions and make smarter decisions on the field. Some examples of on-field analytics include time spent on the court, the number of shots taken by players, where players take their shots and penalty saving percentage. 

Off-field analytics

Off-field analytics focus more on the commercial side of the sport. Off-field data analytics involve inspecting data that is indirectly linked to the game. It involves data associated with businesses, such as ticket sales, financial records, merchandise sales, etc. Some examples of off-field analytics include the number of tickets sold, merchandise inventory levels and the number of viewers.  

How do sports analytics work?

The heart of sports analytics is decoding the numbers and transforming raw data into actionable insights. The process entails collecting, analysing and interpreting the data.  

 

This process starts with data collection through various methods such as wearable technology, video analysis and performance tracking systems. The study then dives deep into this data, employing statistical models, machine learning algorithms and AI and predictive analytics to uncover patterns and trends that may not be immediately apparent. Finally, interpreting this data translates complex metrics into straightforward, actionable strategies that coaches, players and other stakeholders can leverage. 

 

A sports analytics model incorporates the following elements:  

 

  • Data management constitutes various sub-elements like data modelling and data extraction. 

 

  • Data annotation and labelling to label data points to create AI models accurately. 

 

  • Data analytics to analyse gathered data to make recommendations and improve decision-making. 

 

  • Information systems to extract and present outcomes from any data model. 

 

  • Emerging technologies like AI and machine learning generate statistical breakdowns from video content. 

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Sports analytics use cases

There are several different use cases for sports analytics. Here are some of the most common ones: 

 

  • Talent recruitment: Using analytics to identify and recruit the best players. 

 

  • Coaching and player development: Using analytics to optimise play calls and prioritise player development. Analytics can also help athletic executives evaluate players for trades or drafts. 

 

  • Injury prevention and recovery: Using analytics to analyse player workload, biomechanics and medical history to create personalised workout and recovery plans. 

 

  • Sports betting and fantasy sports: Analytics can provide insights for sports betting and fantasy sports. 

 

  • Game strategy: Coaches can use analytics to scout opponents, develop game strategies and target areas for training. 

 

  • Athlete performance tracking and analysis. Analytics can help improve an athlete's game by identifying gaps and areas for improvement. 

 

  • Analysing ticket churn. Organisations can use analytics to measure reasons for ticket churn to retain and acquire fans. 

 

  • Team performance. Sports analytics can forecast the results of matches or games by evaluating team performance. It also points out how well a specific team performs compared to others.

How sports analytics are used in different sports

Each major sport uses sports analytics to analyse players and gain a statistical edge. Here are a few examples.

Data analytics in baseball

Baseball clubs track a comprehensive range of statistics, including batting average, weighted on-base average and weighted runs. These categories help determine how players can impact a game, influence in-game strategies and determine how baseball managers construct entire rosters.

Data analytics in basketball

National Basketball Association (NBA) teams collect several data points to create a detailed profile of each player, informing coaches when it’s best to rest players and keep them healthy for the postseason. Additionally, teams use sports analytics to optimise their offence for three-pointers and layups. 

Data analytics in football

National Football League (NFL) teams use wearables to evaluate player performance, track fatigue and take proactive measures to avoid injuries and wear and tear.  

Data analytics in soccer

Soccer analytics introduced possession value, which gauges how a player’s actions increase or decrease their team’s scoring chances. Leagues like Major League Soccer (MLS) have refined how they measure player performance, considering other variables like player-expected and goalkeeper-expected goals. 

Data analytics in cricket

Cricket teams use sports analytics to identify patterns, strengths and weaknesses by tracking player statistics, ball-by-ball data and pitch conditions. For example, analysts can evaluate a batsman's performance against specific bowlers or pitches or predict how a bowler's delivery will behave based on pitch wear. Data helps teams make real-time tactical decisions, such as field placements or bowling rotations.  

Data analytics in the Olympics

Olympic coaches and teams can use sports analytics to fine-tune athletes' training regimens and techniques through data collection on everything from biomechanics to physiological metrics. In competition, analytics help identify opponents' strengths and weaknesses, guiding strategic decisions in real-time.  

Benefits of using sports analytics

All sports have benefitted in one way or another from sports analytics. Major benefits include:  

 

  • Increased decision-making: Coaches use sports analytics software to look at recorded videos and statistics to make decisions related to building a team, following tactical techniques and player substitutions. 

 

  • Increasing customer interactions: Based on views of online videos and app logins, sports teams might identify patterns in digital engagement, including online sports viewing, to figure out when and what audiences are watching. 

 

  • Create a satisfying watching experience: Simple graphical elements now involve contextual data to help viewers understand the importance of each event during a live broadcast. 

 

  • Analysis of players to boost performance: Analysis of player performance is the most visible region where analytics and data have significantly impacted. Evolutions in this technology over the past year have resulted in accurate and in-depth data gathering, enabling coaches and clubs to measure each player's workload and skills. 

 

Sports analytics thus serve as a bridge between the raw potential of data and the tangible improvement of sports performance. With the help of sports technology solutions, teams get a clearer picture of where improvements can be made, how strategies can be adjusted and what measures can be taken to ensure athletes and organisations are performing at their optimal level. 

Further reading

Check out these resources to learn more about sports analytics and its role in people-centric innovation.

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