A Guide to Modern Football Analytics for Beginners

The term "analytics" might be the most polarizing word in all football amongst fans, pundits,

and even NFL personnel as it's grown more popular across the league in recent years.

Many people chalk analytics up to nerds playing sports on a spreadsheet and scoff at the idea that NFL decision-makers could decide crucial in-game or player personnel decisions based on a math equation.

Analytics is a broad term that doesn't necessarily explain the differences between metrics becoming more common in football analysis these days.

Most haters of "the math" take issue with predictive analytics or win probability models telling NFL head coaches that they should punt, go for it, or kick a field goal in certain situations.

However, there's another world in football statistics that I'll often use on Patriots.com: results-based statistics.

Like total yards, points, or passer rating, there aren't any predictive qualities to these stats that are contrived by what happened, past tense, on the field.

The goal of these metrics is to provide more context into ranking player and team efficiency than traditional raw stats you'll see in a box score. 

For example, there's a significant difference between an offense gaining two rushing yards on third-and-ten and two yards on third-and-one.

One run led directly to fourth down (and likely a punt or field goal), while the other moved the chains for a new set of downs.