Football Ops

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The Game

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Upon further review…

Creating the NFL Schedule

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Big Data Bowl

The annual analytics contest explores statistical innovations in football — how the game is played and coached.

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The Players

Learn how NFL players have changed over time, how they’re developed and drafted and how the league works with them after their playing days are over.  

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The Officials

The Officials

Discover the evolution of professional officiating, the weekly evaluation process and how the NFL identifies and develops the next generation of officials.

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The latest information from the NFL's officiating center.

These Officials Are Really Good

Every week, officials take the field ready to put months of preparation, training and hard work on display, knowing that the whole world — and the Officiating Department — is watching.

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The Rules

The Rules

NFL Football Operations protects the integrity of the game by ensuring that the rules and the officiating are consistent and fair to all competitors.

In Focus: Evolution of the NFL Rules

The custodians of football not only have protected its integrity, but have also revised its playing rules to protect the players, and to make the games fairer and more entertaining.

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The NFL Video Rulebook explains NFL rules with video examples.

2019 NFL Rulebook

Explore the official rules of the game. 6.2.5

2019 Rules Changes and Points of Emphasis

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The NFL's procedures for breaking ties for postseason playoffs.

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The NFL's familiar hand signals help fans better understand the game.   

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A quick reference guide to the NFL rulebook.

Football 101

Football 101

Terms Glossary

Sharpen your NFL football knowledge with this glossary of the game's fundamental terms. 

Formations 101

See where the players line up in pro football's most common offensive and defensive formations.

Quick Guide to NFL TV Graphics

Understand what the graphics on NFL television broadcasts mean and how they can help you get the most out of watching NFL games.

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The NFL’s instant replay review process focuses on expediting instant replay reviews and ensuring consistency. Learn how it works.

Stats Central

Stats Central

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The Extra Point

Welcome to the Extra Point, where members of the NFL's football data and analytics team will share updates on league-wide trends in football data, interesting visualizations that showcase innovative ways to use the league's data, and provide an inside look at how the NFL uses data-driven insight to improve and monitor player and team performance.

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Get a snapshot of the current NFL game stats, updated weekly during the regular season.

The Extra Point

The Extra Point

Welcome to the Extra Point, where members of the NFL's football data and analytics team will share updates on league-wide trends in football data, interesting visualizations that showcase innovative ways to use the league's data, and provide an inside look at how the NFL uses data-driven insight to improve and monitor player and team performance.

Where Do Ball Carriers Tend to Move?

Where do ball carriers tend to move?

The primary theme of the NFL’s second Big Data Bowl, a competition that crowdsources analytical insight into football data, is to predict performance on rush plays. From the moment a ball carrier takes a handoff, where will he end up?

As part of the contest, participants receive snapshots of Next Gen Stats player tracking data from every 2017 and 2018 handoff play. This data includes the speed, angle and direction in which each of the 22 players on the field were moving. One potential input for competitors to consider using in their predictions is the angle in which the ball carrier was moving when he got the ball. As an example, players moving towards the sideline may simultaneously be more likely to be stuffed for a loss and to pick up big yardage (if, for example, he were to outrun defenders). 

One cool plot that can be made from the data (see the statistical code for yourself) is a sonar chart. Initially popularized in soccer by Eliot McKinley, sonars allow us to identify the angles that players tend to move. With ball carriers, the size of each sonar band corresponds to the frequency with which ball carriers are moving in each binned direction group, and the color reflects how successful they are with the ball. Success rate is determined by down and distance (e.g., a three-yard run play on first-and-10 is not a success, while it is a success on third-and-two). 

Here’s a sonar for each player who led his team in carries during the 2017 and 2018 regular seasons. Bands are pointing up, corresponding to players moving from the bottom of the screen to the top, and consist of data from each the past two seasons.

Expand sonar chart.

Expand sonar chart.

Compare the sonars of Gus Edwards (Baltimore Ravens) and Alvin Kamara (New Orleans Saints). Both boast success rates around 60%, but Edwards did most of his damage up the middle, while Kamara tended to get the ball off-tackle. Alternatively, as you’ll see near the bottom right of the chart, then-Atlanta Falcons running back Tevin Coleman (outside runs) and New England Patriots back Sony Michel (up the middle) also offer a nice contrast. 

Information provided in sonars is just one of the many features available for Big Data Bowl contestants to use as they develop their algorithms. Stay tuned for the end of the 2019 regular season, where a live leaderboard will help determine the most accurate predictor of ball carrier success — and the Big Data Bowl title that comes with it.

A Conversation with Two Big Data Bowl Finalists

The 2020 Big Data Bowl is here! And to help mark the occasion, we’re flashing back to the inaugural event to chat with two finalists from the 2019 contest – Nathan Sterken and Adam Vonder Haar. Both contestants earned a trip to the NFL Scouting Combine to present their reports, with Nathan taking home the open entry grand prize and Adam being a finalist.

In addition to their success at the Big Data Bowl, they both also earned new jobs in football analytics following the competition – Adam with the Dallas Cowboys, and Nathan with Telemetry. We spoke with them about their reports, memories from presenting at the Combine and their new roles.

Michael Lopez (Director of Football Data and Analytics, NFL): Tell us a bit about your background in analytics and football.

Nathan: I've always loved football. My playing career peaked in fourth grade so my football background is as a fan. I grew up near Ann Arbor so the University of Michigan is my favorite team. I've been doing analytics professionally for over 10 years in a few industries — from analyzing employment data at the Labor Department to polling and TV data with political campaigns and online behavior with technology companies. 

Read Nathan's winning report - RouteNet: a convolutional neural network for classifying routes

Adam: My interest in analytics and football began about five years ago, around the same time DraftKings and FanDuel were becoming more popular as an alternative to traditional redraft fantasy leagues. I had always enjoyed playing fantasy football with my friends growing up, but it wasn't until I got interested in doing daily fantasy more seriously that I forced myself to learn to code because the copying and pasting into Excel wasn't cutting it anymore. At some point, what was initially a means to an end in coding became what I enjoyed the most, and the analysis became more interesting to me than my results in fantasy football.

Michael: Why did you enter last year’s Big Data Bowl?

Nathan: Lots of reasons. I'd wanted to get my hands on the NFL player tracking data ever since reading about it; I was interested by the research prompt I answered (identify the best receiver routes), since I’d never seen any large-scale analysis of that topic; and since the finals were at the Combine, I figured this was my best shot at getting to run the 40 (and to meet people working in this space).

Adam: The biggest reason I entered last year’s Big Data Bowl was pure curiosity about the data and excitement about the process of working with this new data. Working with tracking data is difficult, and if you don’t enjoy the process at least a little bit, it’s easy to give up on it. I also saw it somewhat as a challenge to myself and as a learning opportunity to improve my skill set. Lastly, there was definitely some motivation around potentially going to the Combine and getting my work in front of teams, but both of those felt like such a long shot to me that I couldn’t rely on them for my main motivation.

Read Adam's report - Exploratory data analysis of passing plays using NFL tracking data

Michael: Give us your one paragraph elevator pitch — what was your entry about last year?

Nathan: I demonstrated that you can automate some of the classification work that is typically done by hand and then analyze plays at scale — specifically, I trained a computer to recognize receiver routes using the player tracking data and then computed efficiency metrics for route combinations. The finding: throw it deep (or at least threaten to).

Adam: My entry last year was centered around exploring several potential techniques to discover how an offense can get receivers more open. I used several different methods to classify receiver routes, looked at how different combinations of routes result in greater separation for receivers, and used convex hulls to characterize defender spacing.

Nathan Sterken walks through his winning report at the 2019 Big Data Bowl.

Nathan Sterken walks through his winning report at the 2019 Big Data Bowl.

Michael: What is one memory of the event that stands out as you look back?

Nathan: Seeing head coaches in the hallways was fun, but the stand-out moment was discussing my research with the analytics staff from several clubs and the other folks who attended the event. I made connections with several clubs and got to meet a couple of the authors I cited in my paper. 

Adam: One of my favorite memories of last year’s event was meeting the many brilliant, like-minded individuals who also submitted entries. Being able to talk football and football analytics specifically in-depth, listening to novel ideas everyone had, and, in general, just hanging out with each other at the Combine made the experience especially memorable.

Michael: Next Gen Stats can be tricky. What would your recommendation be for a first-timer dealing with NFL player tracking data?

Nathan: Football coaches have the best advice here: take it one play at a time. If you start by focusing on one play (preferably one where you can find video for it so you know exactly what happened) and doing some simple plots and statistics, you can get your arms around the format of the data without worrying about the size of the overall data set. That said, you’ll want to do that exploration in a programming language like Python or R because you’ll need to be able to quickly scale up your analysis to crunch through each play. 

Adam Vonder Harr discusses his report, Exploratory data analysis of passing plays using NFL tracking data.

Adam Vonder Harr discusses his report, Exploratory data analysis of passing plays using NFL tracking data.

Adam: As tempting as it can be to dive into analysis immediately, I think it’s important to take your time cleaning and understanding the data as much as possible first. The tracking data is not without anomalies and errors occasionally, and even the good data often must be modified so that everything is oriented and normalized consistently. Spending the effort to prepare the data thoroughly makes the analysis much easier when you get to that point.

Michael: Tell us a bit about your new roles with Telemetry and the Dallas Cowboys, respectively. How have you been able to help?

Nathan: Telemetry helps clubs with game preparation and player analysis by making it quick and easy to find video of any type of play by any player. Specifically, they use the player tracking data to create a rich set of annotations on each play and then pair that with a searchable video database. I’ve helped them by building modeling pipelines that use the player tracking data to identify additional aspects of the play, like the type of coverage that was played by the defense. 

Adam: I am now a Football Research Analyst with the Dallas Cowboys. Our team’s responsibilities range from scouting and personnel, to assisting coaches with game planning and opponent scouting, to sports science and player health. I spend a lot of time working with the tracking data specifically, trying to discover ways we can use it to make our processes more efficient and improve our ability to make evidence-based decisions.

What Can Player Tracking Data Tell Us About the Onside Kick?

Throughout NFL history, it wasn’t uncommon for kickoff teams to line up like they were kicking deep, only to shock both fans and opponents with an onside attempt. Famously, Thomas Morstead of the New Orleans Saints surprised the Indianapolis Colts at the start of the second half of Super Bowl XLIV with an onside kick (one that the Saints recovered). In total, from the start of the 2010 season through the 2017 season, the league averaged 9.4 surprise onside attempts per season.

In 2018, however, there were only five surprise attempts. Through the first four weeks of 2019, we’ve only seen one. 

The likely culprit? Changes to the NFL’s kickoff rules.

Prior to the 2018 season, NFL Player Health and Safety and special teams coaches worked together to reimagine the kickoff play — one designed to increase player safety — resulting in a multitude of changes regarding player alignment and allowable blocking types. Concussions on kickoff plays dropped by 35 percent in 2018, but the formation redesign potentially made it more difficult to recover onside attempts. In addition to the drop in surprise onside kicks, the kickoff team has only recovered 6.1 percent of non-surprise attempts since the start of 2018, a rate lower than the league’s historical average of 12 percent. In 2019, teams are 0-for-9 at onside kick attempts through Week 4 (not including recoveries that were nullified by penalties).

During last year’s offseason meetings, the NFL’s Competition Committee debated the merits of a follow-up rule change, proposed by the Denver Broncos, that would have given teams the opportunity to use one scrimmage play per game in place of an onside kick. Although the proposal was not adopted, our football data and analytics crew was able to provide the Committee with insight into how and why onside kicks have changed.

Check out the below animation, which overlays Next Gen Stats tracking data on onside attempts for the kickoff teams and the football, averaged over the course of each of the 2017 (pre-kickoff changes) and 2018 seasons (post-changes). Dots in red correspond to players in 2017, while the blue dots represent players in 2018. Only onside attempts to the kickoff team’s right side are included. 

Three differences stand out.

First, there are six red dots on the right side, compared to five blue dots. As part of the kickoff rule changes, five players must now line up on each side of the kicker.

Second, players on the kickoff team may no longer get a running start, which is why the blue dots (2018 season) begin the play standing still.

Finally, and perhaps most importantly, without the running start, players from the 2018 season are no longer able to get down the field as quickly, giving them less of an advantage over the receiving team. 

Altogether, each of the above findings provide an explanation for why we are seeing fewer surprise onside kicks and lower recovery rates. Although Denver’s proposal for an alternative did not pass last year, the NFL will continue to use analytical insight to drive potential rules changes.

Q&A with 2019 Big Data Bowl College Winners

The first-ever Big Data Bowl brought together talented members of the analytics community – from college students to professionals – to crowdsource innovative and data-driven football ideas. For today’s Extra Point, we interviewed the college entry winners from Simon Fraser University – Matthew Reyers, Dani Chu, Lucas Wu and James Thomson – to hear more about their experiences and findings. The group’s first-place report, Routes to Success, modeled play success rate and expected points under various passing combinations.

Michael Lopez (Director of Football Data and Analytics, NFL): Tell us a bit about your background in analytics and football.

James: I’ve played football since high school, first as a defensive tackle and back, later in rec league flag football in college. It’s been my favorite game to play since I was 14. I got interested in sports statistics during my undergraduate degree in statistics, when I started following baseball more closely. I quickly turned to football, and since the beginning of my master’s in statistics, I have been active in football statistical research.

Dani: I’ve played sports my whole life, but my introduction to football was mainly through fantasy football. While I have played football in a casual setting, I have not played in an organized environment. I started statistical research in sports in 2016. I have a background in math, computer science and statistics.

Related: Read the group's winning report - Routes to Success

Matt: In elementary school, I thought I was going to be the next NFL star. It took all of five games to realize that wasn’t the case. I played for a few more games after that but stuck with other sports. When I eventually got to University, I stumbled across statistics and a group of people that liked sports as much as I did. Since that class four years ago, I have been applying statistics to as many sports problems as I can get my hands on, regardless of the sport. 

Michael: Why did you enter last year’s Big Data Bowl?

Dani: I was excited to work with the NFL’s tracking data. I found the question that was asked technically challenging and important for football teams to understand. I thought it would be a great way to challenge us and demonstrate our skills.

James: The opportunity to work with data more detailed than typical play-by-play data I was used to was too good to pass up.

Matt: Tracking data offered a new and exciting field of study for me. I had the chance to view football in a way not many others had been able to and to possibly unearth insights for the first time. I felt myself to be on the cutting edge of analytics and found that to be one of the big drivers of joining this competition. 

Lucas: Player tracking data provides us a great opportunity to analyze sports in a way more granular fashion. We’re able to answer a lot of interesting questions and to extract meaningful insights from the tracking data. This is a great venue for us to bring our analytics/data-science skills into sports and showcase the potential of sports analytics. 

Michael: Give us your one paragraph elevator pitch – what was your entry about?

Dani: Our project could be broken down into three sections: Route Identification, Route Combination Evaluation and Quantification of Openness/Field Control. Route Identification is useful for a variety of reasons, including tagging game film, building novel receiver metrics and for evaluating route combinations. Route Combination Evaluation could be used to help inform new plays or determine the efficiency of current plays. Finally, the quantification of field control can be used to help evaluate both the design and execution of both running and passing plays.

Michael: What is one memory of the event that stands out as you look back?

Dani: It’s hard to choose one. The dinner the night before with the other finalists was a great way to get to know everyone. The walkthrough of the presentation rooms beforehand was great. To see our names on the banner was surreal. Seeing the crowd and knowing most of the NFL teams were represented was awesome. Finally, walking by [Dallas Cowboys head coach] Jason Garrett in the hallway was very cool.

Learn more about the 2019 Big Data Bowl

James: Standing in the presentation room looking around at all of these amazing people who we were competing with was surreal. Everyone's work was so inspired. There are so many good ideas and avenues to explore this data that I had never thought of until I got there.

Matt: If I had to focus on one standout feature, it would have to be the meet-and-greet poster conference after the presentations. Being from a smaller school and Canadian, I rarely have the chance to talk with NFL representatives and affiliates about the inner workings of the game. What I learned from my conversations in that brief hour-long session has fueled many new research questions going forward.

Lucas: It was awesome to be selected as one of the finalists and fly to Indianapolis to present our ideas to NFL personnel. The conversation and the feedback we got from them were valuable for us to develop our ideas further down the road.

Recap of the 2019 Big Data Bowl

Michael: NextGenStats can be tricky. What would your recommendation be for a first-timer dealing with NFL player tracking data?

Dani: My recommendations would be to:

  1. Start small. Build out your work for one play. Write a function for that one thing and then generalize it to all plays.
  2. Get rid of unnecessary data as soon as possible. Drop columns you aren’t using and rows you don’t need.
  3. Build small functions to do useful things and give yourself a toolbox/package of functions to work with.

James: Make a function for every different data transformation you do. Keep them very general so that no matter where you are with your code it will work. It’s a complicated data set, but it’s simple to manage with smaller steps.

Matt: Visualize frequently. We had sample code for creating GIFs of plays that proved invaluable. When we were working, we chose a few plays that we could also find on YouTube and built our intuition about the data and its real-life counterpart. Tracking data is nearly uninterpretable in table formats — I feel it needs to be seen to be understood. 

Lucas: Start with the most intuitive things. For instance, visualize the movement of players and ball, then move on to distance travelled, speed and acceleration, etc. It’s also a good idea to do pair-programming, where you always have someone looking after you. 

Michael: Tell us a bit about your summer job working with the NFL. 

Dani: Working for the NFL this summer was unreal. We got to work on technically challenging material and are helping inform change in the NFL. It is great to see an investment from the league in data-driven changes.

James: I never thought that I would find myself working for the NFL, let alone while I was still a student. It was great to talk about how our work could be used to help make decisions based on player movement in the NFL.

Matt: The NFL gave us a wonderful opportunity to expand our ideas even further after the Big Data Bowl this summer through a project with them. We had the opportunity to explore more positions than just eligible receivers, describing movement patterns across the game. Our work felt important to the game and the NFL, truly putting us at the cutting edge of analytics. I have nothing but positives to say of this wonderful experience. 

Lucas: It’s incredible to see our Big Data Bowl submission leading to our opportunity working with the NFL. We are able to explore the movement patterns of different positions. It’s truly exciting to further develop our statistical methods, learn how to leverage cloud computing and deliver our analytics results to the NFL. 

Where Your Favorite Team Likes to Pass the Ball

When an NFL quarterback drops back to pass, where is he likely to throw the football?

For decades, scouts would scour film looking for insight into answering this question. Left-handed quarterbacks would presumably favor the left side of the field, whereas other passers were seen as preferring the deep ball. Most recently, Carolina Panthers quarterback Cam Newton came under questioning for not throwing downfield at all

With the advancement of NFL Next Gen Stats, which tracks every player – on every play, anywhere on the field – at a rate of roughly ten frames per second, scouts are no longer the only football personnel who can measure pass locations.

The following two charts use Next Gen Stats data to compare each team’s passing locations from the 2018 season to the overall league average. For technical followers, two-dimensional kernal density estimation was used to smooth the data. 

First, here is each AFC team. In the top left, we see that the Buffalo Bills’ chart features several deep throws, perhaps not surprising given that Buffalo quarterback Josh Allen can chuck it 75 yards. Alternatively, the Oakland Raiders and their quarterback Derek Carr tended to focus on shorter throws near the sideline (see bottom right).

WHERE AFC TEAMS THROW

Next, here is each NFC team. 

In the bottom right of the below graphic, the Seattle Seahawks, led by Russell Wilson, stand out as a team that, instead of throwing deep or short, tended to mostly throw to the left side of the field. Alternatively, in the top-right, the Tampa Bay Buccaneers, with both Jameis Winston and Ryan Fitzpatrick behind center, featured a greater rate of intermediate throws (5-20 yards) than any other team in the league.

WHERE NFC TEAMS THROW

While pass location for each team is one interesting way of using player tracking data, the league also uses it to inform other aspects of the game, including rule changes and the position of officials. For now, as your favorite team steps back to pass, know that wherever the ball lands, the NFL is tracking it.

Using Data and Analytics to Identify High-Impact Fouls

As the NFL began its offseason, several club rules proposals sought to expand the use of replay to include penalties. In response, the NFL Competition Committee used data and analytics to identify fouls that have the biggest impact on game outcomes.

To assess impact, the committee used win probability, also known as game winning chance. Below is one example. The black line (New Orleans Saints) and gold line (Pittsburgh Steelers) in the animation show the game winning chance for both teams at each point in the game during their 2018 Week 16 matchup.

The game was back-and-forth. New Orleans led at halftime, only to see Pittsburgh regain the lead late in the third quarter. With 1:25 remaining in the game, a Michael Thomas touchdown reception led New Orleans to a 31-28 win. Amidst all this action, two of the seven most impactful plays — as judged by the change in game winning chance from one play to the next — were defensive pass interference calls.

First, at the end of the first quarter, this call on Pittsburgh’s Joe Haden increased New Orleans’ game winning chance by 14 percent, from 43 to 57 percent.

Then, with two minutes left, this defensive pass interference foul by Haden on Thomas increased New Orleans’ win chances by 21 percent.

In reviewing game winning chance data across several seasons, the committee found that both defensive and offensive pass interference tended to have disproportionately large impacts on changes to each team’s game winning chance.

Armed with this and other information, the Competition Committee prioritized these two fouls as it looked to expand replay to include penalties. Under the new rules, both calls on Haden would be reviewable, as would potential offensive and defensive pass interference calls that were not made on the field.

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