Data Science Effect on Professional Sports
Published: 2022-01-04 17:07:40

Data Science Effect on Professional Sports

Over the past couple decades, many of the trends and evolutions of the games sports fans love so much have been sparked by data analysis. In 2002 a middling Oakland Athletics MLB team put together an American League record 20 game win streak. The Athletics used an analytics method called "sabermetrics" to help find replacement players for three stars the team lost in the offseason. Soon other baseball clubs were using similar methods and quickly the practice of using tracking data and complex machines that can accurately predict stat leaders and clubs league standings are being used in combination with well informed coaches who understand the game and the analytics. When this additional information is adopted appropriately it can very well be the difference between wins and losses. Sports data analysis techniques enable users to find valuable information from all sorts of data, using various algorithms in order to statistically calculate the big data and extract important analytical data to suit their needs. 

Most professional soccer clubs outsource the data mining to third party vendors who at first began analyzing data for fans and media outlets. As the technology advances the algorithms and machines are able to take in and make sense of more data. Methods like tracking data document each players every move on the pitch. Analysts are also adding hundreds of soccer terms to the machines so it knows every type of pass, set piece, or tackle and it is defined in the machine. That level of tracking is like having a scout for each individual player on the pitch. This is leading to the recommendations based off the data analysis to become more accurate. Analysis can also help clubs save money by pointing out players with comparable production. This is especially beneficial to smaller clubs who can’t attract or afford select stars. Sports management analytics often deals with how to allocate scarce resources, maximize revenue, or minimize costs under constraints. Mathematical programming models are deterministic meaning they utilize known fixed parameters for the constraints such as salary caps, the number of players on a roster, and the number of players in the lineup.

Some tools data analysts and sports analysts use in order to evaluate are Python data manipulation methods and pre-processing to clean the data before performing data science methods on the data to gain tactical insights. Different data mining methods have been applied to uncover hidden relationships, patterns, and laws in sports data. Analysts also specialize in biometrics, GPS, radio frequencies, and accelerometers to assess total activity (miles run, steps taken, or average speed) by players in a game or practice. Databases (SQL and NoSQL) in a data intensive data driven world searching and selecting data has become as important as sampling. Gathering and making use of information is what sports analysts and data scientists do every day. They understand information technology as well as statistical modeling. Whenever communicating with management, sports analysts must move beyond formulas, numbers, definitions of terms, and the magic of algorithms. Sports analysts should be able to transform complex models into simple and straightforward language that is easily understood by others. Sports data analysis focuses on discovering value from data and providing valuable information and conclusions for enterprises and managers. This information is then visualized to display these valuable sports insights. Making a story out of the data is arguably the most important part of the process because numbers and stats alone do not mean much to the average manager.

The more analytics are normalized and with more data, machines will continue learning and will be able to process more terminology and return more effective and accurate suggestions. Good sports analysts are also good data scientists who use code, databases, algorithms and machine learning to get the most probable return. The best data analysts are able to take the data and tell a story using pictures, graphs, color-coded spreadsheets, or whatever best illustrates the findings to the decision makers. Analytics can very well be the difference between wins and losses for any given team in any sport. 


Works Cited

Bai, Zhongbo, and Xiaomei Bai. “Sports Big Data: Management, Analysis, Applications, and Challenges.” Complexity, Hindawi, 30 Jan. 2021, https://www.hindawi.com/journals/complexity/2021/6676297/#conclusion. 


Connors, Tom, and David Nicholson. The NBA Data Scientist. Bloomberg Quicktake, 17 May 2019, https://youtu.be/MpLHMKTolVw. Accessed 4 Jan. 2022. 


Coolkingsingh. “Top 10 Technologies for Sports Analytics.” Medium, The Sports Scientist, 5 July 2020, https://medium.com/the-sports-scientist/top-10-technologies-for-sports-analytics-c093105daeda. 


PerformanceFFT. “The Numbers Game | How Data Is Changing Football | Documentary.” YouTube, YouTube, 22 Dec. 2017, https://www.youtube.com/watch?v=lLcXH_4rwr4.