The Role of Data Science in Sports

While they may seem completely unrelated, data science and sports go hand-in-hand. Players, team managers, coaches and fans rely on sports analytics before making decisions or developing strategies to win games.

Sports are competitive so it’s no wonder many types of statistics are meticulously kept on file to see which players or teams can beat records. For example, Joe DiMaggio holds the longest hitting streak in Major League Baseball at 56 games in 1941.

In 1984, Eric Dickerson set the record for the most rushing yards in a single NFL season at 2,105. While Adrian Peterson came close with 2,097 yards in 2012, Dickerson still holds the record.

Data science isn’t just used in sports to fuel competition between professional players; it also plays a key role in improving game quality, fan experience and player safety. Thanks to these emerging applications, students who earn advanced data science degrees may find themselves providing crucial services to their favorite teams and helping to evolve sports for a new generation.

Data analytics have changed the game and are vital in helping team managers, coaches and players ensure they’re prepared to win. Since preparation is key to winning, professional teams take sports analytics seriously and gather as much data as possible to ensure they have a competitive edge. Some of the most important data that teams analyze before a game include:

Professional sports teams work hard to gather relevant data to prepare for games. There are many ways players, teams and fans use statistics and data to enhance their position.

To improve performance, players keep track of their own statistics and analyze how they played in previous games. Nutrition, training hours and game performance produce different types of statistics, such as how fast the player runs, how much weight they lift, or how much protein they ate during the day.

By tracking this data and comparing it to how they felt on game day or how they performed, players can make changes to their training routines or diet to get better at their sport. When all players focus on their own performance analytics and pinpoint how to improve, this analysis and the changes that come with it help prevent organizations from becoming the most disappointing sports team in the league.

Each player must be focused on individual performance but playing together as a team is also crucial in securing a win. When teammates adopt data science together, they can analyze how they perform together.

Coaches may experiment with player combinations to see if better statistics are achieved with different lineups on the field. For example, if an MLB player catches 90% of a teammate’s throws to first but only 45% of another teammate’s throws, the coach is likely to pair the more successful partners on game day.

Using data analytics, team managers can develop machine learning techniques to identify winning player combinations and successful strategies.

Sports is a business and the more engaged the fans are, the more profit organizations experience. By learning about data analytics in the online world, sports management teams can discover how and when fans are likely to attend events or buy merchandise.

Management analyzes social media patterns, attendance and merchandise sales to better understand what consumers want out of the game. This allows them to identify what’s important to fans, such as an engaging mascot or unique merchandise. Management can then be sure to provide these amenities to keep fans satisfied and coming back.

With fans in mind, management can develop marketing strategies and advertising campaigns that target these consumers. Data helps them to easily identify fans that are likely to engage with the team so they don’t spend advertising dollars on consumers who aren’t interested in their sport.

Fans who gamble on sports may find data science and statistics helpful when placing bets. When gamblers can analyze a team’s past performance with accurate statistics, it’s much easier to predict when and where the team will be successful in the future.

Without having to blindly choose which team or player will perform well, sports fans are more likely to engage in gambling. Statistics allow them to develop a prediction method driven by data, making gamblers feel more confident betting on certain teams or players.

Athlete injuries can wreak havoc on a team’s season or record. When star players go down due to a preventable injury, it’s frustrating for coaches and can negatively affect the player’s career. While some injuries are unavoidable, data analytics helps players and medical professionals learn when and how injuries are most likely to occur.

For example, 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 better understand when players are overexerting themselves and to create strategies to avoid exhaustion during games.

With this information, players may identify weak spots in their form so they can be more cautious when playing. Sport medicine employees may also analyze how they treated injuries to better understand the success rate. They may change treatment plans for certain injuries or players to try to speed healing.

Talent scouts typically observed potential recruits practicing or playing competitively. However, data visualizations and analytics that include statistics on past performance are also a vital part of the scouting process. Professional scouts can’t always physically visit every promising collegiate player, so they rely on statistics to identify and prioritize who to visit and observe.

Data is an important part of the sports industry for players, coaches, management, sports medicine workers and fans. Not only can data analytics help teams win games, these statistics can also help improve player performance, prevent injuries and encourage fans to attend games.

Last updated: September 2020