This is an example of the season Expected Points Added (EPA) tracker for an individual team. This plot details their offense (red) and defense (blue) adjusted EPA values, as well as their Early Down Pass rate (yellow). This allows for easy detection of trends within a season such as post-bye changes, hot streaks, and relative strength of each side of the ball.
Next is an example of one of the Wide Receiver Clustering Plots from the Wins Above Replacement shiny app. This highlights a player's values within a handful of metrics from the top going counter-clockwise; routes run (volume), targets per route run (usage rate), yards per route run (efficiency), touchdowns per route run (scoring efficiency), average depth of target (how far downfield are their catches), yards after catch per reception (after catch ability).
Finally, is an example of offensive and defensive EPA over a handful of seasons broken down by team. This plot highlights team trends on each side of the ball.
This plot shows average depth of target (ADOT) vs time to throw for Quarterbacks in 2020. This is instructive for understanding what their respective offenses are asking them to do and how quickly they get rid of the ball.
This plot shows ADOT vs Expected Points Added (EPA) on plays without pressure for Quarterbacks from 2018-2020. This is instructive for seeing different trends within the group as a whole, such as that there is generally a positive correlation between a higher adot and a higher passing EPA, as well as highlighting specific players, such as Jamies Winston's penchant for chucking the ball downfield and Josh Rosen's struggles even without pressure.
Last is a plot showing Offensive EPA compared to Percentage of plays within opponent territory. Initially I figured there would be a positive trend as better offenses would get into opponent territory more, but the data is actually pretty strongly against this idea. Actually, better offenses tend to spend less time on the opponent's side of the field because they are either generating explosive plays that score from further away or are far more efficient at moving the ball and therefore spend less time getting bogged down and settling for field goals.
Here is a list of other random analyses that I have completed in the past.
HAVOC Rating: Looking at rates of bad things happening for the offense (int, fumble, sack, negative yard plays)
Team clustering for playoff success: Comparing teams in a PCA approach to cluster them on a handful of features to estimate playoff success
Interceptions in a row: See how many interceptions in a row have occurred. Additionally, effective pick sixes in a row.
Receiver Rolling EPA: Comparing WR EPA over specific time periods.
Rushing Scheme impacts on holding calls: Do outside zone calls get more holding calls than runs up the A-gap.
Three and out Percentages and Value: Seeing the true value and impact of a three-and-out and which teams do it most often.
Full NFL Redraft Rankings: Given a blank slate, which players would you draft first?
Best Divisions in Football: Comparing the divisions and seeing which are the best from a handful of factors.
QB Sneak Rates: For both college and pro, who sneaks most often and best?
Playoff Kryptonite: Which team does your team absolutely not want to see if the postseason? (GB-SF, BUF-KC, etc.)