The world of sports has always been about numbers and this can change the approach of teams and individuals. An old school of people who think they’d rather judge with their eyes and simply look at the scores has been replaced – lately, there’s been a shift from simple scorekeeping to something much deeper and more complex. Enter data science, the latest player on the field that’s completely transforming the way sports are run. 

Anybody who has seen the movie Moneyball has seen the struggle between gut feeling and older methods and the newer data-driven way of analyzing it.

For years, coaches and analysts relied on their instincts and experience to make decisions. Today, they’ve got a new teammate: data. This Moneyball approach showed that traditional scouts can potentially miss things. 

There are lots of reasons why the top sports teams in the world are now using data all the time. 

F1: A Prime Example

Few sports have as many different factors in terms of numbers as Formula 1. It is a sport where every fraction of a second counts, and data science is proving to be a crucial part of the race strategy. 

F1 has always relied on engineering prowess and driver skill, modern teams now have a secret weapon in the form of advanced data. Each race car is essentially a rolling laboratory filled with sensors capturing every detail, from tire pressure to fuel consumption to the G-forces experienced in corners. 

The top Formula 1 racers are examined to the highest degree and every time they take to the track there is more data presented to the teams to use and make the next choices. During a race weekend, F1 teams collect gigabytes of data from the car in real time. Telemetry data is wirelessly transmitted back to the pit wall. This includes information on the car’s speed and the driver’s braking and acceleration patterns. Engineers and data scientists use this data to make crucial decisions about strategic decisions – when to pit and which tires are the right choices are some of the decisions that need to be made.

Aston Martin’s racing team has provided a brilliant insight into what it is like to work in this field, too. Formula 1 is also a very wealthy sport which means that there is a lot of money to put into developing teams of people who may be able to help analyze this level of data. 

Performance Analytics in Other Sports

A lot of people don’t realize that data science plays a big part in everyday life, so it makes perfect sense that the top sports are a hub of data analytics. In basketball, analysts look at shot charts to identify the best areas of the court for players to take shots. These charts use data to show where each player has a higher probability of scoring or making an impact on the game.

Cricket isn’t so big in America but it is a sport that thrives on statistics and has also embraced data science. Analysts examine variables like swing and the way that pitch conditions make an impact.

In both cases, the data is used to not just to look back at past performance, but to predict future outcomes. Machine learning models sift through thousands of previous matches to forecast how a player might perform under specific conditions. It adds a whole new level of preparation and anticipation to the game.

Coaching and Statistics Roles

Coaches today are drowning in a sea of data points that come from every corner of the pitch. GPS trackers measure player movements and even the impact of their tackles. Heart rate monitors give real-time feedback on player fitness and wearables have become massive in sports. In some instances, drones are capturing aerial footage to analyze formations and spacing. New wearables are also being produced in order to feed this data and give teams the edge. Some of the sports are now taking place away from the field. 

This data isn’t just for show. It’s helping coaches and statisticians make their decisions faster and more accurately. Imagine a coach knowing exactly when a player is about to hit their fatigue threshold and substituting them before an injury happens. Or think about using predictive models to understand which lineup would work best against a particular opponent. Models that analyze huge levels of data are even getting better. 

Conclusion

Sports are worth a lot of money and big teams can be worth billions. It makes sense that at this elite level, there are so many ways that people are trying to get ahead. Those few percentage points can potentially make a huge difference and this is why everybody is looking to make the marginal gains. Data scientists may have a huge part to play in shaping some of the biggest sports people love to watch.