Data Science in Sports

To most of us, sports are a source of exercise and joy, something that we catch up with after a scorching day at work. But the Sports industry is also a massive revenue generator, valued at well over 500 Billion Dollars through direct sources of revenue alone. In today’s Sporting world, Data Science is used to construct teams, win tournaments, assess the resources, and ultimately increase the profits garnered. A competent Data Scientist is an intricate part of any successful team, and franchises are approaching Data Firms to enhance their performances. Let us look at some of the most definitive functions that Data Science plays in shaping the Sports Sector.

  1. Scouting and Recruitment: One of the key factors behind a team’s strength is its new recruits. While most teams prefer purchasing youngsters due to better stamina and greater malleability, there have been instances where recruiting underrated veterans has reaped gold, as in the case of Billy Beane, the manager of the Oakland Athletics baseball team. Beane, along with Paul DePodesta, recruited seemingly inefficient players, who would eventually go on to win a record-setting twenty games in a row (This came to be known as the MoneyBall method).

The factors taken into consideration vary from the athlete’s current and future health (physical, mental, and emotional), individual vs. team dynamics, and game simulation, to social dynamics, public image, etc. Since it is physically impossible for the recruiters to visit every ground and fetch probable recruits, the selection process is aided by data in the form of previous records. This data coupled with the existing prediction models offer a solution to whether a player is suitable or not for the team.

2. Tracking athletes’ performance, health, and safety : Any physically taxing activity has a lot of possibilities for injuries, and this holds true especially for Sports. An injured player becomes a liability for the side, and teams are looking at ways to both reduce the chances of an injury as well as deal with them in a systematic and efficient way.

Apart from physical injuries, high-pressure situations also cause emotional and mental strain to a sportsperson. Well-planned nutritious meals, a good sleeping routine, energy to train and play, the right training exercise regimes, and the ability to tackle the mental challenges that accompany the world of sports are some of the basic qualities that are expected out of a valuable player.

Data Scientists analyze the strengths, weaknesses, and mannerisms of a player through video footage, records, and recently, wearable gadgets. For instance, players with the NBA’s Minnesota Timberwolves wear Fitbit health tracking watches during practice to collect data on heart rate, sleep, and movement. The team analyzes this data to understand when players are overexerting themselves and thus create strategies to avoid exhaustion during games.

Photo by Zoë Reeve on Unsplash

3. Player, team and fan analysis: 

Exemplary data analysis has often been the difference between the winning and losing teams in several high-profile tournaments including the popular Indian Premier League, where the Data Analysts are seen as a major reason behind a team lifting the cup. Rajasthan Royals, one of the least expensive teams on paper, ended up winning the inaugural edition, and pundits attributed this to the excellent application of existing data about the opponents as well as their own members.

Functional parameters like how fast players run, how much weight they lift, or how much protein they ate during the day, can be tracked and this data is compared to how they felt on game day or how they performed. Using this correlation, players can make changes to their training routines or diet to get better at their sport. Coaches may experiment with player combinations to see if better statistics are achieved with different lineups on the field. Taking the example of football, the conventional combination of 4 defenders + 3 mid fielders + 3 attackers has often been replaced by a different approach and has resulted in successful outcomes.

At the end of the day, sports is a business, and the more engaged the fans are, the more profit organizations experience. By using data analytics, sports management teams can discover how and when fans are likely to attend events or buy merchandise. Understanding the sensibilities of the fanbase and acting accordingly will popularise the franchise exponentially. In this sense, the fans can be seen as ‘customers’, and the team as the ‘brand’.

4. Sports Gambling: Gambling in sports is a domain in which a lot of money flows, and Data Science plays a key role in determining where to put that money. Sports gambling makes up around 13 percent of all gambling globally, accounting for more than 700 billion dollars. Accurate statistics give the stakeholders an idea about the recent form of a player/team and the conditions which will be encountered during the game.

Data sourced over years of a sport can be consolidated to provide insightful results about which team will possibly win and which player has a good chance of performing well. Without having to blindly choose which team or player will perform well, statistics allow in developing a prediction method driven by data, making gamblers feel more confident betting on certain teams or players.

Click here to find amazing sports datasets to try your hand in predictive modeling and visualization.


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