People may view SQL as outdated. Others may see it as a reliable, trustworthy old tool that never breaks down. Regardless of where you stand on the matter since SQL first appeared in the 1970’s, a lot has changed.
Gone are the days where professional athletes made below $100,000 USD. Gone are the times where athletes recovered by eating a couple of lobsters or 6 pork-chops. Pencil and paper gave way to number riddled spreadsheets (Moneyball illustrates this quite well), which now in turn has given way to sensors in athletes’ uniforms and databases.
Databases have grown as well. They’ve matured, and moved on from being just a data management system. We’ve seen them transition to navigational, to relational, and now – to non-relational. (Databases have even influenced their own sports related language – Sports Data Query Language.)
What do databases offer the sporting world?
Keeping it short and simple, databases offer more:
- and scalability.
Structure: databases can be set up to ensure only specific types of data are recorded, and you can automate the recording process.
Consistency: related to the automation of data, you can ensure that data integrity is preserved across channels.
Accessibility: while staying consistent, databases let multiple people view and work with data simultaneously.
Scalability: as the amount of data collected changes, databases can be easily expanded as needed.
How does this affect the world of sports? Professional athletes are constantly monitored, tracked, and produce valuable bits of data. This data can help athletes and their teams understand anomalies in performance, create more effective training regimes, and improve recovery time – just to name a few. With all this data being thrown about, what good would all this be – without a good way to structure it?
SQL Databases in Sports
So how has SQL been able to break into the world of sports?
- Want to work for the same organization as living NBA legends Gregg Popovich and Tim Duncan? Well, the San Antonio and Austin Spurs need candidates with SQL skills – and maybe a nice jump-shot.
- How about working as Senior Analytics Specialist for one of the largest sports networks in North America? SQL is essential – don’t forget to practice your anchor voice, just in case.
- More evidence? A quick search at Indeed.com using the keywords “Sports, Data, Analyst” and “SQL” gave just under 1,000 results.
To put it bluntly, SQL is “considered a must” for those considering a sports analyst career (in addition to being able to work with the likes of Python, C#, Java, just to name a few other requirements ).
Case #1 – Major League Soccer
Recent years have seen professional teams such as the Seattle Sounders of Major League Soccer fame began incorporating SQL Server to structure and read data just a few years ago. The team uses GPS trackers and Omegawave to help track physiological data and fatigue in order to better plan training regimes. These devices are worn in vests on the training pitch and as a wearable off it. Data is then stored on their own SQL Server database, giving the team a comprehensive overview of all recorded data. This allows the team’s analysts to draw insights and further influence the team’s training and recovery.
Case #2 – Oracle in America’s Cup
Another good example? Oracle. Thinking a bit outside the box, the tech giant saw an opportunity and has repeatedly sponsored an American yacht racing team. Unsurprisingly, the team trains using 1,000 real time sensors to collect data on sailor health, weather, and ship-integrity. This data is then stored on Oracle’s Exadata. The data can then be given to the team in real-time, course-planning and playbooks, helping them claim top positions in the America’s Cup.
Case #3 – FIFA World Cup
One final example? The years 1994 and 1998 and the FIFA World Cup. 1994 saw the world’s biggest sporting spectacle being hosted by none other than the United States – a country who didn’t even have a professional football league at the time (the MLS was founded 2 years later). The 1998 World Cup was held in France. SQL Server was used as the database both times and hosted a barrage of football data, some of which dated back to 1930. Starting in 1994, anyone with access to a computer and the internet would have access to a combination of video, graphical, and other historical football data, all within a matter of seconds (or several minutes, seeing as this was during the 90’s).
Stay tuned for Part 2 where we dive into NoSQL, MongoDB, and their current impact on sports!