Match-ups: How has a player contributed versus an opponent? How does the player’s opponent defend his position each night?
Pace: How fast/slow does a team and/or its opponent play?
Volatility: What is the player’s floor/ceiling in different statistical categories? On average, what can I expect from a player on a game by game basis?
Recent performance: How well has a player faired in his last 5 games?
At first glance, asking all these questions (and others that I haven’t thought of yet) about the data could prove to be a daunting task. Not for Qlik. The most powerful feature about Qlik Sense and QlikView is the Associative Experience. Having this capability allows me to quickly find insight into my questions and it also allows me to pivot to other questions as they pop into my head. Also, the chart library allows me to visualize the data at different levels of granularity.
As an example, let’s look at scoring.
Using this bar chart, I can see the scoring averages for all the players. And while this is useful, it could be misleading. If James Harden had a handful of outlier games that were significantly higher than his normal scoring range, his scoring average will be skewed because of those outliers.
The bar chart just doesn’t give me enough information to answer my questions. What is his floor? What is his ceiling? What range can I generally expect this player to deliver? Well, to dig deeper, let’s look at the distribution plot