Where there are games or sports, there is data. Lots of it.
Consider the Super Bowl, on which an estimated 23.2 million Americans will place a bet. Or the British Grand Prix (F1), which saw betting participation more than double between 2018 and 2022. Also consider how many times the odds change in a given match of any kind, like mixed martial arts (MMA), where the favorite at the start of a match can become the odds-based underdog by the second round, resulting in a flood of new wagers.
By the way: the odds of someone picking all 63 March Madness winners?
One in 9.2 quintillion. (A quintillion is a one followed by 18 zeros).
All of this sports-based unstructured data requires very solid and flexible data storage, but we’ll get to that later. First, let’s dive into the data behind sports and why it can be so hard to wrangle.
The Massive GDP of Sports Betting
Sports is a billion-dollar industry that includes massively popular sports betting platforms and fantasy sports leagues such as DraftKings and India’s Dream11. DraftKings alone made more than $2 billion in revenue in 2022—a billion dollars more than the previous year.
Overall, the global sports betting market accounted for $83.65 billion in 2022 and is expected to grow more than 10% annually until 2030, making it roughly the size of the energy drink market. That’s a massive increase in participation and also a huge opportunity. But to fully capitalize on it, sports companies need to be able to handle this massive influx of rapidly changing unstructured data—which isn’t exactly a slam dunk for many.
Sports and Unstructured Data
Consider golf. It’s not the sport that typically comes to mind when you hear the word “data,” but that’s because most people probably don’t realize just how much is going on on the green. Golf tournaments use all kinds of unstructured data to track performance and calculate odds in real time, including pitch angle, pitch speed, shot accuracy, shot distance, weather data, and more.
Then there’s Formula One, which is one of the most data-driven sports on earth. The data behind the average F1 race is simply mind-blowing. As Mercedes-AMG Petronas F1 Team Leader Toto Wolff himself said:
“The best driver can only perform if he has access to reliable data.”
The average F1 car has about 300 onboard sensors and produces 1.5 terabytes of data per race weekend and about 11.8 billion data points per season. This massive data pool is used to create predictive models that incorporate historical data to make recommendations on tiny adjustments that ultimately improve the car’s (and the driver’s) performance.
Unstructured data also comes into play for sports wagerers. Think of the movie Moneyball, where the movie’s central character was a genius data cruncher who knew how to use data to improve the team’s performance. People who make sports bets can use the same data to make the most informed bets, say, in golf, where a certain player may have a significantly lower accuracy for 6-meter puts versus 5.8-meter puts, or in baseball, where the plethora of data about players’ on-base percentage, hits, and error rate can all be used to make better predictions about a game’s outcome.
Sports Betting Platform Data Challenges
There are many types of data involved in sports, in addition to that around betting and odds. All of this data is considered “unstructured” because it’s not arranged according to a preset data model and thus needs to be arranged into columns and rows to make it machine-readable and available for analysis.
There’s tremendous value in being able to rapidly process and safely store unstructured data. In sports betting platforms, for example, participants will often get “payouts.” These payouts need to happen quickly and accurately or the sports betting experience will be significantly compromised.
Furthermore, the opportunities for personalized, real-time offers in sports betting platforms are abundant and require being able to process and store massive amounts of unstructured data.
Handling all of this unstructured data requires a very solid, yet flexible, data storage infrastructure, such as that provided by FlashBlade//E™. After all, if you can’t properly retain it, you can’t use it, and if you can’t use it, you can’t capitalize on it or the opportunities of sports data. You’ll miss your shot.
Case Study: Sportradar
Sportradar is a Swiss-based company that collects and analyzes sports data for bookmakers, national and international sports federations, and media companies. The company used Pure’s flash storage solution to improve the performance and management of storage infrastructure for its platform.
FlashArray™ rapidly delivers data to the servers while Purity ActiveCluster™ provides synchronous mirroring for data protection to meet business continuity requirements. This allows Sportradar to save a significant amount of time and effort, especially when it comes to adding new clients.
How the SF Giants Leverage Data
Baseball is a game replete with data, from ERAs to home runs and on-base percentages. That’s why the San Francisco Giants use Pure to gain a competitive edge for a number of critical systems at the park. They also keep players healthy off-season via analysis of player biomechanics data.
There’s another aspect to using data in professional sports, and that’s improving the customer experience. The Giants also use Pure to get the most out of their historical data analysis to improve the fan experience via tailored ticket offers, incentives, and engagement with themed nights and events.
Pure: A Slam Dunk for Sports Applications
Pure’s FlashBlade//E delivers exactly the kind of data storage all sports applications need: affordable, flexible, secure, and scalable. FlashBlade eliminates the complexities of disk for file and object repository workloads and delivers a best-in-class user experience and at the lowest long-term cost.
- Unparalleled efficiency: Easily manage your unstructured data growth and maximize the value of your unstructured data while saving energy, space, and money.
- Effortless scalability: FlashBlade’s scale-out data repository is optimized for multi-petabyte workloads, allowing you to slash operational overhead without increasing complexity or having to add additional management resources.
- Non-stop innovation: FlashBlade gives you cloud-like simplicity and flexibility with added control so you can seamlessly integrate leading hardware and software.