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Although most might say that the area of sports is purely a physical game, I believe that sports are slowly becoming more of a game of numbers. With the wealth of information to be analyzed from sports records, analytics is beginning to transform sports in new ways.
Analytics does work in sports. Analytics is used in sports through real-time video for athlete performance analysis, enables dynamic stadium ticket pricing, and develops machine learning models to improve player behavior. However, analytics currently only works in sports at the professional level.
Analytics has more than just worked in sports, it has transformed the way teams and athletes train themselves.
I had a look at the growth of the Sports Analytics industry on Mordor Intelligence and was surprised to see expected exponential growth from 0.72 billion in 2019 to USD 3.51 billion by 2025.
Upon further research, I dug out some really refreshing ways in which the use of data analytics has helped improve sports. Let’s begin with some background on sports analytics before I reveal more on the applications. If you’re as curious as me, read on!
What is Sports Analytics?
Sports analytics is the use of data analytics tools for optimizing any sports-related metrics. This field all began from one man named Bill James, who began the used of applied statistics on baseball. The term “sports analytics” was coined in a popular film – “Moneyball”.
An area that resurfaced recently with the boom of the increasing amount of data being produced from training records, the field of sports analytics is increasing in popularity among players, managers and even fans like you and I.
Traditionally, I would say that most sports were analyzed through basic means, for lower-level tracking of player performance. Some of these include shooting efficiency for soccer and ball possession.
These startups have gone beyond the basic level of descriptive statistics to powerful AI machines for prediction.
In general, the area of sports analytics can be naturally separated into On-field Analytics and Off-field Analytics.
On-field analytics involves the use of metrics to optimize performance such as player fitness. Off-field analytics can be seen as the business operation intelligence side of the industry. It typically involves sales metrics or stadium management.
If you’re still unsure of what’s this whole buzz about sports analytics, check out this video below from a Youtube channel (Ken Jee) by a Sports Data Scientist that I personally subscribe to.
Where Has Analytics Worked in Sports?
1. The Major League Baseball (MLB)
MLB is a huge sports area itself and you bet they’re going to jump on the bandwagon to implement sports analytics in their teams. Since the implementation, there has been more analysis done on top players. This resulted in a “lengthening of the prime” for these players, according to this podcast by Wharton’s Abraham Wyner and former MLB player Brendan Harris.
Additionally, more MLB teams have seen younger talent being able to take on leadership roles instead of senior players. The use of analytics has proven that player performance does indeed decrease as players age, according to Harris.
2. Grand Prix Motocycle Racing (Moto GP)
The Moto GP space has also seen no lack of use of sports analytics. For example, Ducati partnered with Accenture to focus on building up data for AI in hopes of improving performance. What an unlikely collaboration, I know! But things then started to better when bikes were attached with sensors that collected data during test races.
With such a huge load of data, much of it can be used to feed into an AI machine to give performance insight. Imagine the improvements that could be made! By letting that bike run over different conditions, engineers could modify bikes to improve overall speed and ultimately, lap times.
This video talks about how Ducati has collected data using their bikes and are even implementing the new changes in consumer bikes.
3. The National Football League (NFL)
Ah the NFL, where do I begin?
“Manage the Game”Every NFL analysts’ advice to average quarterbacks
Similarly, the NFL has also seen more teams picking up sports analytics by expanding their analytics departments. For example, the Baltimore Ravens have been recognized as the most analytically-inclined team by ESPN. This doesn’t come up as much of a surprise for me though, since the Ravens have one of the largest analytics teams among the teams in the NFL. Raven’s Data Analyst, Daniel Stern, is one of them, who works at the different probabilities and analyzes for the optimum performance at each game.
This focus on analytics has brought the Ravens to them to the highest single-season third- and fourth-down conversion rates in all of NFL. Now, this if this doesn’t prove that sports analytics works then I’m not sure how else to convince you at this point.
Even sports skeptics like New York Giants General Manager Dave Gettleman, has been bought over to the idea of sports analytics after seeing its success. He has since made plans to hiring some tech guys to look into his scouting methods.
“Information is power.”Kevin Stefanski, Cleveland Browns Head Coach
With the information all ready to be analyzed from all of their existing records, teams have realized the power of information derived from collected data.
I found this video below while doing my research on the NFL, do check it out!
4. Real Madrid
If you’re a fan of Real Madrid, this is great news for you! If not, then well, you’ll just have to sit this part through. Recently, in a large collaboration, football club Real Madrid has joined hands with Microsoft in hope of a digital transformation. As one of the greatest football clubs in the world, Real Madrid is set on utilizing their data to gain a competitive edge over their opponents (sorry for the bad news for some of you guys).
Some applications of sports analytics they have include; predicting and preventing injury, analyzing for peak performance, maximizing training using data and tracking fan behavior. This definitely sounds like quite promising to me! Ranging from on-field analytics to off-field analytics, the team is set to win both the champion title as well as the hearts of fans.
Yup, I didn’t forget about the NBA. This one’s for all you basketball fanboys. The NBA is undoubtedly one of the most analytically-involved leagues among those I mentioned above. For example, the Golden State Warriors’ huge involvement with analytics has brought in an increase in 3-point hoops.
NBA even had a hackathon in 2019. Now that’s a real commitment to analytics.
Can I Benefit from Sports Analytics as a Fan?
Of course, you can! You can benefit quite a lot from being just a fan. You can benefit from dynamic ticket pricings to get your tickets at the right timing at lower prices. Additionally,
Here is a simple resource: The Great Analytics Rankings by ESPN , where you can learn in more detail about the level of analytics involvement of your favorite teams. Also through analysis of fan behavior on fan engagement platforms, big teams are able to better understand you and your needs, like access to player statistics.
Phew, that was quite a packed post with all the useful applications of sports analytics. Although the examples are great, they do not stop there. Imagine being able to see your favourite team or athlete compete with his/her best?How cool is that?
I hope you’ve at least found some of them interesting, if not here’s a great video to inspire you.
My Favorite Learning Resources:
My Recommended Learning Platforms!
|Learning Platform||What’s Good About the Platform?|
|1||Coursera||Certificates are offered by popular learning institutes and companies like Google & IBM|
|2||DataCamp||Comes with an integrated coding platform, great for beginners!|
|3||Pluralsight||Strong focus on data skills, taught by industry experts|
|4||Stratascratch||Learn faster by doing real interview coding practices for data science|
|5||Udacity||High-quality, comprehensive courses|
My Recommended Online Courses + Books!
|1||Data Analytics||Google Data Analytics Professional Certificate||–|
|2||Data Science||IBM Data Science Professional Certificate||–|
|3||Excel||Excel Skills for Business Specialization||–|
|4||Python||Python for Everybody Specialization||Python for Data Analysis|
|5||SQL||Introduction to SQL||SQL: The Ultimate Beginners Guide: Learn SQL Today|
|6||Tableau||Data Visualization with Tableau||Practical Tableau|
|7||Power BI||Getting Started with Power BI Desktop||Beginning Microsoft Power BI|
|8||R Programming||Data Science: Foundations using R Specialization||Learning R|
|9||Data Visualization||–||Big Book of Dashboards|