Updated Primer For Understanding Analytics Recaps and Previews

As we use analytics to better evaluate the Notre Dame football team, there are going to be some statistics that you will need to get the most out of the articles we are posting. This list will provide not only definitions of the stats, but some context for what we will use the stats to learn and how they can be made actionable in future weeks by Irish players or coaches.

• Expected Points Added (EPA): This measures the difference in the expected points a drive is worth before and after each play. This link provides an excellent detailed explanation (credit to Alok Pattani and ESPN for the detail). Essentially, it takes into account the down, distance, and yard line among other factors and calculates the expected number of points a team scores from that position. EPA is simply the difference in Expected Points at the start of a play and the end of it.
• Success Rate: A play is considered successful if it generates positive EPA.
• Personnel: The first number refers to the number of running backs, the second number refers to the number of tight ends, and the number of wide receivers can be inferred from knowing these two numbers. We do not have access to All-22 film so this is based on alignment at the snap. For example, if Tommy Tremble is lined up in the slot in a receiver’s stance he is considered a receiver and not a tight end.
• Average Depth of Target (aDOT): The number of yards downfield a receiver is when the ball is thrown to them. This is used to get a sense of where on the field the team tends to attack the opponent, and break down a quarterback’s performance to see how well they are controlling various aspects of the game (screen game, deep passing, etc.)
• First Down Rate: The percentage of plays of a given type resulting in a first down.
• Garbage Time: The vast majority of our charts will only include plays where Notre Dame’s win probability is between 2-98% according to the win probability model provided by cfbfastr, the source of many of our data analysis tools. We do this in order to focus on plays that are actually impacting the outcome of competitive games.

For fuller context of EPA/play and success rate here are a series of charts showing Notre Dame’s 2019 statistics in these areas, the national leader’s statistics, and the FBS average. Please note that in the rushing EPA/play chart the FBS average is 0.00 EPA/play, so the bar is not missing it is just zero.

As shown, even slightly positive EPAs indicate above average performance, although some elite teams are able to generate points consistently above zero. Notre Dame was far more efficient passing than rushing in 2019, although the FBS as a whole was only slightly better off throwing than passing. Believe it or not, Alabama, LSU, and Clemson were all pretty good football teams in 2019.

Finally, let’s focus on some new statistics we have incorporated to analyze Irish and opponent running games.

• Box Count: The number of defenders in the box on a given play, which is defined as the area in between the tight ends and five yards off the line of scrimmage. This can get a bit subjective, but we’re trying to look at how many run first defenders are committed to each play.
• Blocker Count: Any down linemen or tight ends, and running backs only if they are being used to lead block for a QB draw or another player running the ball on that play.
• Blocker Advantage: This subtracts box count from blocker count to see how many more blockers on a play Notre Dame has than its opponent. This is typically negative, since the offense has a quarterback subtracting from its total and the defense does not.

For example, on this play shown above Florida State has a blocker count of five, just its offensive line. If it had a tight end or a fullback it would be six, but on this play it only has five. Note that the quarterback is not a blocker, putting Florida State at a deficit. Notre Dame has five players on the line, and a linebacker three yards off the line of scrimmage in the box for a blocker count of six. This gives Florida State a blocker advantage of -1, which is the most common.

All of these statistics can be used in conjunction with traditional stats to gain a fuller picture of what is going on on the field for the Irish. If you have further questions or want to comment on the positives and limitations of these statistics, please leave a comment and we will be happy to try to expand your analytics knowledge even further!