How to Leverage Graph Databases for Pro Sports Analysis

If you are an athlete, coach, or have even the slightest interest in sports, then this article will not only be captivating but might also change your perspective on what it takes to improve your natural athletic abilities.

Firstly, we must understand the answer to the question,” what is a Graph Database?.”

Graph Databases- Explained

Graph databases are different from other NoSQL databases because they can work with entities of any kind, store both vertices (e.g., people) and edges between these vertices (e.g., friends), support a wide variety of queries, and make it possible to represent rich domain models.

While what’s stated above is certainly impressive, you might find it even more staggering that graph databases are especially good at managing relationships, which adds significant value when looking for an edge in pro sports.

How Can Graph Databases Help Improve Games 

What is a graph database, and how can it help with games? The techniques presented here are merely examples of how graph databases can help you improve your game.

Example: Player Profile Graphs

One common task that athletes undertake during each off-season is analyzing their games to identify their strengths and weaknesses.

For most professional athletes, there is a discrepancy between what they think or believe they are good at and what they actually do well on the court, field, rink, or ground. 

Reviewing your performance objectively is crucial because it allows you to understand better what it takes to play at the level you expect from yourself.

There are many valuable tools and metrics for this kind of analysis, but what if you could harness the power of graph databases to analyze our games? Using a data model that represents each player by their statistics, scores, and ranks in various categories, you can use graph traversals to calculate what is most important for each player.

Ask questions like:

• What are your best statistical categories?
• Which ones would you rank at the top of all players in your role?
• What are the most common player rankings when comparing different players’ statistics across multiple categories?

These might sound like complex questions, but you can execute these queries quickly and easily in milliseconds with graph databases.

For example, what percentage of all players with a height under six feet make it to the NBA? And what percentage of all European players who have played in the NBA have made it into their respective national teams?

Again, these questions might sound complex, but graph databases can help you quickly and easily answer them.

Example Two: Twin Tower Effect Graphs

Many of the greatest duos throughout NBA history have had what’s commonly referred to as “the twin tower effect.” What exactly does that mean?

As one might expect, the twin tower effect is when two players with similar heights line up together on the court to form what’s commonly referred to as a “tandem” or double-tower lineup. Teams have observed this effect for decades, and while it does add an advantage on offense, what is it that makes this lineup so effective?

Two players with similar heights playing on the same team will most likely have very similar statistical profiles. Players who are too different in height or other metrics statistically tend not to make it very far together on the court.