Sports Analytics: Adding New Dimension to the World of Sports
Sports Analytics is slowly changing how Sports Teams can leverage Technology and Data Analytics to their Benefit
The sports industry is a multi-billion dollar industry today. The crowds at stadiums, online and live cheer for favorite teams or sports players, humorous commentary, and other thrills, are familiar aspects in every sports event. But did you know that data analytics is also gaining prominence in every other sport too? Companies leverage data science and data analytics to predict trends, understand customer behavior, draw insights to make well-informed decisions, and more. Following similar lines, sports analytics too involves using the data related to any of the games or sports such as players’ statistics, weather conditions, information from expert scouts, etc., and building predictive models around it to make informed decisions.
Sports analytics first entered pop culture when author Michael Lewis wrote a book called Moneyball, after getting inspired by the general manager of Oakland Athletics leveraging in-game statistics to predict player potential and assemble a strong team on the small recruitment budget. His strategy helped the A’s make the playoffs. Moneyball was later made into a 2011 sports-drama feature film starring Brad Pitt, which portrayed how a baseball coach, Billy Beane rebuilt his team against all odds using empirical data and statistical analyses on players’ performance. This sparked how sports analytics can be employed in sports and bring impressive results in terms of unlimited opportunities.
Drawing parallels with the Moneyball movie, the English Football club too used sports analytics to search and recruit undervalued soccer players. The team’s director of research, Ian Graham, designed a proprietary model that calculates how every pass, run, and goal attempt influences a team’s overall chance of winning. Liverpool uses a metric called ‘goal probability added’ to assess players. The research team analyzes whatever action a player does on a pitch – whether it’s a pass or a shot or a tackle if the player is a defender. Speaking to the Freakonomics podcast, Graham explains, “We ask questions: What was this team’s chance of scoring a goal before this action happened?’ And then, ‘What was this team’s chance of scoring a goal after that action happened?’ And we call that ‘goal probability added,’ which is a really catchy name.” They use optical tracking to get information on every ball touch that every player makes in a game, where it was on the pitch, and what happened next. All these data are used to sports analytics on the team and determine a player’s performance. All these played Liverpool win the 2019-2020 Premier League championship.
Even the US Tennis Association is using IBM Watson to run sports analytics to improve their tactics on the court as well as their training regimens.
Sportswear Manufacturer Adidas has developed a system called miCoach that works by having players attach a wearable device to their jerseys. The data amassed from this device shows the coach who the top performers are and who needs rest. It also provides real-time stats on each player, such as speed, heart rate, and acceleration. Such data can help trainers and physicians plan for better training and conditioning of the sports players. Even tech companies like Zebra Technologies and STATSports have come up with player tracking devices that capture metrics both on-field or while training in real-time that provides timely insights and bespoke training plans.
In basketball, NBA employs RSPCT’s shooting analysis system, which uses an Intel RealSense 3D depth camera mounted behind the top of the backboard and connected wirelessly to a small computing unit and tracks and analyzes every shot, including trajectory and location. When used in combination with Kinexon’s wearable wristband technology, coaches can get a full end-to-end understanding of player position, performance, and wellness on the court. The camera accurately tracks when and where the ball strikes on each basket attempt. It directs that data to a device that displays shot details in real-time and generates predictive insights. This allows viewers to understand shooting accuracy faster and better without having to dive into analytics and also clearly see groupings of shots and why a shot is made or missed.
It is evident that sports analytics is used primarily to improve team performance and enhance the chances of winning the game. Also, sports analytics is not only limited to using it to understand the team performance but also gain insight about the fan-base of big teams and use it to catch the eye of investors and boost the fan-interaction.
For instance, using social handle data from say Facebook and Twitter, companies can find patterns and form clusters/groups using several clustering algorithms, within the fan base and run campaigns on target groups or simply increase the fan base via engagement events. Brands and sports teams can further mine sentiment via social media streams to understand what fans are thinking and can use the data to connect with them, sell sponsors’ products and merchandise. Moreover, this data can influence marketing efforts and decisions about sporting events, like scheduling games, serve custom-made ads, and broadcast content catering to fan preferences.