If you’ve ever gone through a job search in the data/analytics world, you’ve probably seen these two job titles (and slight variations on them) quite a bit: Data Analyst and Data Scientist. But what exactly is the difference between these two roles? The short answer is it depends. In reality, these job titles are quiteContinue reading “Data Analyst vs. Data Scientist: What’s the Difference?”
Author Archives: Brendan Kent
Sports Analytics Reading List
This list was compiled through a combination of my own experience and recommendations from other members of the sports analytics community. I’ve broken the list into two sections: “Where to start?” This section contains my personal essential sports analytics reads. These are books I’d recommend regardless of your skill level or sport interest. “Where toContinue reading “Sports Analytics Reading List”
Free Sports Data Sources
One of the first steps in any analytics project is acquiring the right dataset. While much of the more advanced data (e.g. player tracking data) remains inaccessible to the public, there’s still a significant amount of free data only a few clicks or a couple of lines of code away. Two weeks ago, I publishedContinue reading “Free Sports Data Sources”
Sports Analytics 101: Metric Framework Examples
Sports Analytics 101 is a series of blog posts outlining the core concepts behind sports analytics in non-technical terms. You can find all available installments in the series here. Over the previous six posts, I introduced a “The Metric Framework,” a framework for thinking about an individual sports analytics metric. Here, I’m going to apply theContinue reading “Sports Analytics 101: Metric Framework Examples”
Sports Analytics 101: Blind Spots
Sports Analytics 101 is a series of blog posts outlining the core concepts behind sports analytics in non-technical terms. You can find all available installments in the series here. In an earlier post, I introduced a framework for thinking about an individual sports analytics metric. This framework is essentially mental “paperwork” to fill out whenever youContinue reading “Sports Analytics 101: Blind Spots”
Sports Analytics 101: Adjusting for Opportunity
Sports Analytics 101 is a series of blog posts outlining the core concepts behind sports analytics in non-technical terms. You can find all available installments in the series here. In an earlier post, I introduced a framework for thinking about an individual sports analytics metric. This framework is essentially mental “paperwork” to fill out whenever youContinue reading “Sports Analytics 101: Adjusting for Opportunity”
Sports Analytics 101: Productivity vs. Style
Sports Analytics 101 is a series of blog posts outlining the core concepts behind sports analytics in non-technical terms. You can find all available installments in the series here. In an earlier post, I introduced a framework for thinking about an individual sports analytics metric. This framework is essentially mental “paperwork” to fill out whenever youContinue reading “Sports Analytics 101: Productivity vs. Style”
Languages and Tools to Learn for Sports Analytics
One of the first questions most sports analytics newcomers have is: Which languages and tools do I need to learn to be successful in the field? Learning to code can be a big time investment, and most folks understandably want to make sure they’re spending time on the important stuff. With that in mind, I’veContinue reading “Languages and Tools to Learn for Sports Analytics”
Sports Analytics 101: Descriptive vs. Predictive
Sports Analytics 101 is a series of blog posts outlining the core concepts behind sports analytics in non-technical terms. You can find all available installments in the series here. In an earlier post, I introduced a framework for thinking about an individual sports analytics metric. This framework is essentially mental “paperwork” to fill out whenever youContinue reading “Sports Analytics 101: Descriptive vs. Predictive”
Sports Analytics 101: Facts and Proxies
Sports Analytics 101 is a series of blog posts outlining the core concepts behind sports analytics in non-technical terms. You can find all available installments in the series here. When using the metric framework (introduced in the previous post) to analyze a metric, one of key things we need to establish is whether a metric,Continue reading “Sports Analytics 101: Facts and Proxies”
Brendan Kent 