Data plays a fundamental role in almost every industry. The global sports industry has also evolved over time, with managers and teams becoming more and more receptive to using data to gain a competitive edge. The global sports market is one of the biggest in the world, and it’s expected to balloon to $440.77 billion in 2021.
Popular sports like football, soccer, cricket, tennis, and hockey are watched by audiences all over the globe. There’s big money involved, and larger teams are always looking to find a chink in their opponent’s armor. Thanks to comprehensive Sports Data Analytics, that is now more possible than ever.
With increased spending, sports teams are now able to move away from more conventional methods of analyzing the opposition. Instead of simply limiting research to watching how certain teams play, sports teams are able to invest in comprehensive analytical data that they can use to evaluate their competitors’ performance.
This article will provide you with an in-depth understanding of what Sports Data Analytics is, how numerous sports teams are using it to their advantage along with a list of predictions that can be made using Sports Data Analytics.
Table of Contents
- Introduction to Sports Data Analytics
- Predictions That Can Be Made Using Sports Data Analytics
Introduction to Sports Data Analytics
Sports Data Analytics is simply the study of analytical data involving players and their performances in order to determine their weaknesses and strengths. Sports Data Analytics is now used in a variety of different ancillary industries, and there’s significant potential for growth. In fact, it’s expected that the Sports Data Analytics market is going to reach upwards of $4.5 billion by 2025.
One of the most publicized examples of the use of Sports Data Analytics comes from Billy Beane. As the General Manager of Oakland Athletics, he used market inefficiencies to his advantage, using sabermetrics like on-base percentage to sign players on low prices and turn his team from a group of has-beens into contenders for the World Series.
The marriage of technology and Sports Data Analytics, thanks to the increased processing power, Machine Learning principles, and Artificial Intelligence, has opened new doors for teams to assess every little detail about players. In fact, data is now available for both in-game activities as well as previous games through wearable sensors and Cloud processing power.
Some of the world’s leading tech giants now work closely with the larger sports teams to produce wearable technology that helps them assess player performance and individual metrics, especially regarding their health. This also allows them to monitor fitness levels in granular detail, something that wasn’t possible before.
These devices and sensors are available in all kinds of sizes. Players don’t even have to stick them on their bodies; they can be woven into the fabric of their jerseys, installed in sports equipment like balls or bats, or even in their shoes. Data is then transferred in real-time, allowing coaches to monitor performance from the sidelines and make informed decisions on the go.
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Predictions That Can Be Made Using Sports Data Analytics
The backroom staff actually relies on Sports Data Analytics more than ever before, and that’s primarily because of the fact that analytical data makes it much easier to predict several key events. It also helps in making big decisions, especially when a club is looking to buy a big-name player. Sports Data Analytics can be leveraged for the following applications:
1) Injury Predictions
The use of wearable technologies in sports is increasing, and this makes Sports Data Analytics much more predictable. As you can see in the graph, a study was conducted using Zephyr BioHarness Wearable Technology to gain a more comprehensive insight on how to predict and prevent injuries by assessing the overall mechanical load and the BMI of the wearer.
This can help identify players with a higher risk of injury, allowing fitness staff to balance their overall exertion levels and enroll them in conditioning programs if needed. The use of Logarithmic Regression models via Binomial Distribution is commonly used to assess the probability of player injuries.
Using multiple platforms and comprehensive analysis software, analysts can obtain Neuromuscular data and store it in a Data Warehouse. This data can be tracked throughout the season to identify changes. Deviations can be identified using Convolutional Neural Networks (CNN).
2) Player Valuations
There are numerous factors that combine to create a player’s market value. Their overall hype and brand name, level of performance, and consistency, all play an essential role. When a team decides to make a sizable financial investment in any player, they must have data available that justifies their investment. Using a data-driven approach, smaller teams are now able to compete in the big leagues by just buying the right players. Scouts must compile play-by-play data and then run them via Machine Learning algorithms to make predictions.
Using Text-based Sports Data Analytics and Predictive Modeling, scouts can better determine whether a prospect would be a viable fit in the team’s existing system. For instance, a star player with a higher risk of injury might not be a viable investment for a smaller team that wants someone to be available throughout the season and lead the team. It’s important to understand that the technology used depends on the league and its stature. In the National Basketball Association, cameras are installed at multiple angles to record the gameplay at every second. These cameras are oriented towards different parts of the playing surface, with variable frame-rate recording.
The location of the ball and the players can then be extracted using Machine Learning algorithms. On the other hand, the National Football League installs chips in the ball and the players’ shoulder pads in order to track their location across the pitch throughout the game. By comparing the location of the ball and the player, comprehensive data can be extracted for predicting drives, screens, and even linemen blocks. Other information, such as running speed, average heart rate, and total distance covered can also be tracked.
This data can then be used to determine just how effective a player has been in-game and how consistent they have been over the season. This can lead to Predictive Modeling that helps analysts figure out whether a player is a one-season wonder or whether their performance is getting better over time. This can also help managers predict which player performs better in different conditions.
3) Team Strategy
The image above is a Heat Map of Arsenal’s 2020/21 season. Using Analytical Data and Visualization techniques, it’s easy to see that Arsenal likes to play out from the back, passing the ball out to their defenders and eventually into the midfield.
They also rely quite a bit on their wingers, running the ball wide instead of preferring to take it through the center. When evaluating the opposition, the use of such Analytical Data becomes incredibly important. This data puts teams in a position to predict how the opposition might set themselves up for the match and how they prepare for different in-game situations.
Through the use of trusted insights and data collected across the course of the season, teams can better gauge their competition and tailor their strategy to neutralize their opponents. Data Science is now playing a significant role in helping teams increase their win ratio.
4) Evaluating Ticket Churn
Customer Retention is almost always cheaper than acquiring new ones. Sports teams and clubs now use Logistic Regression models to determine Ticket Churn. Paired T-tests, on the other hand, can be used to determine how specific promotions and campaigns will impact ticketholders and overall customer engagement.
This makes it easy for sports teams and clubs to predict the percentage of season ticket holders that aren’t likely to renew their membership for the next year. This allows clubs to better predict their ROI based on how the team performs on the field. For instance, poor on-field performances are obviously going to impact game attendance. Predictive Data Modeling can be used to gauge that impact.
5) Ticket Pricing
Gate receipt revenue is a significant income stream for most businesses. Using historical data and Performance Correlation models, sports teams can better understand how a change in pricing will affect fan engagement and gate receipt revenue. Occupancy rates can be evaluated depending upon the competition, the company’s performance, as well as the impact of any big-name signings.
6) Sports Betting
Sports Data Analytics is also leveraged by betting companies to evaluate the odds for different events that happen in-game. The sports betting industry is worth more than $1,000 billion and continues to grow. From recreational betting aficionados to avid gamblers, the industry caters to all kinds of users. As events occur in-game, betting algorithms manipulate the odds in real-time to reflect the performance better. The massive amount of data available is constantly processed through several predictive models by betting companies to balance the odds offered to players.
Technology is becoming more and more pervasive, and the industry is only expected to grow as more accurate sensors and analytical tools become available. Almost all of the big teams have invested heavily into Sports Data Analytics. This has led to the rise of private analytics companies that offer the data on different players up for sale.
This article provided you with a comprehensive understanding of how sports teams across the world are leveraging Sports Data Analytics to improve their performance. It also provided you with a list of the key predictions that can be made using Sports Data Analytics.
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