Director of Football Data and Analytics National Football League
New FeatureThe 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’s the best spot on the field to complete passes… and not get picked off?
In analytics circles, the middle of the field is generally considered to be the best place to throw if you want your receiver to catch the football. But a quarterback’s job goes way beyond completing passes. A QB also has to read the defense, avoid pressure and sacks, prevent interceptions and pick up first downs.
Turns out, not only is the middle of the field the best place for a receiver to catch a pass, it’s also the best place for an opponent to intercept a ball, too.
The following plots look at the likelihood of a completion (left plot) and an interception (right plot) based on the pass distance (in yards) and direction (left, middle, right). Regular season games from 2010 through Week 13 of 2019 are included (bin sizes with smaller samples are dropped).
In both plots, lighter colored areas correspond to more completions or more interceptions. Both middle columns feature lighter colors when compared to the columns on the left and right. That implies that throwing down the middle yields higher rates of completions, but it also yields higher rates of interceptions.
More specifically, a pass thrown 20 yards down the middle of the field has about the same completion percentage as one thrown 10 yards towards either sideline. But those same passes thrown down the middle of the field are also about four times as likely to be picked off. Alternatively, nearly one in five passes are picked off when the ball is thrown 30 yards down the middle of the field, which is about three times the rate of interceptions on the sideline.
As more years of tracking data are observed, we’ll be able to more precisely identify the best parts of the field to complete passes (Ex: Where your favorite team likes to throw the ball), as opposed to relying on play-by-play categorizations. For now, know that when a pass is thrown to the middle of the field, it’s less likely to be incomplete.
Frank Gore Keeps on Running
Over the last two decades, the median age of running backs has dropped two full years, from 26 years, 7 months (in 2000) to 24 years, 7 months (2019).
Meanwhile, Frank Gore, at the age of 36, just keeps on running.
Check out the following chart, which highlights the age distribution of ball carriers in each season with at least 150 carries (or 100, in 2019). Player ages are shown as of September 1 in each season. While the curves have moved slowly left over time, the trend of getting younger holds for all but one player — Gore.
Initially drafted in 2005 by the San Francisco 49ers, Gore was the third youngest running back in his rookie season, when he logged 127 carries for 608 yards. He’s topped 125 carries in each season since. In contrast, of the 20 running backs that were likewise drafted in 2005 with Gore, only one — Darren Sproles — recorded a single rush after the 2014 season.
Over the last four seasons, Gore has finished as the oldest back with at least 125 carries. It’s no surprise that with Gore being so productive for so long, his place on the NFL’s all-time rushing list is secure; this past weekend, Gore passed Barry Sanders for No. 3 on the NFL’s career rushing leaderboard.
Aaron Rodgers Takes Advantage of Free Plays Better Than Anyone
Aaron Rodgers is the NFL’s career leader in passer rating, a two-time MVP and seven-time Pro Bowler. But there’s one thing he’s even better at than passing the ball — taking advantage of the “free play.”
As the quarterback is calling signals, an early jump by a defensive player is a five-yard defensive offside penalty. If the center snaps the ball before that defensive player is a threat, the offense can still run a play. This instance is called a "free play" because even under the worst-case scenario — say, an interception — an offense can still go back and take the initial five-yard penalty.
Since 2006, Rodgers leads all quarterbacks with 84 passes thrown on free plays. And no one has attempted more deep free shots than Rodgers, either. Check out the plot below, which highlights the air yards each quarterback has thrown for after drawing an offside penalty from the defense.
Each of the 170 quarterbacks with at least one free play in his career is shown in the plot with the grey lines, with six standout quarterbacks highlighted.
Rodgers stands alone. Beginning in 2010, he has thrown for approximately 225 air yards a season on offside calls alone. More precisely, roughly once every other game Rodgers earned a free shot downfield, averaging 25 air yards on these throws. No quarterback is within 900 yards of Rodgers in total on this stat since 2006.
Rodgers owns the top three individual seasons in air yards on free plays, peaking in 2015, when he attempted 16 passes for a total of 407 yards downfield (25.4 air yards per free play). Of those 16, seven were complete, including two touchdown passes to James Jones.
“Free play — Rodgers has made a living on this,” said ESPN’s then-Monday Night Football announcer Mike Tirico when calling one of Jones’ touchdown grabs in 2015 (video below). “Draw the offside, know it’s a free play, take the shot downfield.”
While Rodgers has owned air yards on free plays over the last decade, one young quarterback has started on a blazing path — Kansas City Chiefs signal caller Patrick Mahomes. In 2018, Mahomes tossed 15 passes after getting a free play, completing nine, and throwing the ball a total of 234 yards downfield.
Which quarterbacks have been the least aggressive off of an offside call?
Current Minnesota Vikings quarterback Kirk Cousins has thrown just 13 career passes off a free play, which traveled a total distance of 87 yards downfield (6.7 air yards per free play). Deshaun Watson (six passes, 56 air yards, or 9.3 air yards per free play), Kurt Warner (12 passes, 48 air yards, 4.0 air yards per free play) and even Peyton Manning (17 passes, 121 air yards, 7.1 air yards per free play) stand out as the opposites of Rodgers.
How Do NFL Coaches Use Their Challenges?
After an unsuccessful challenge of an incomplete pass on Thursday Night Football against the Los Angeles Chargers, Oakland Raiders head coach Jon Gruden fell to 0-for-7 on challenges in the 2019 regular season. But Gruden is not the only one with limited success — through Week 10, 13 of the league’s 32 head coaches have yet to win a coaches’ challenge this season.
With that in mind, here’s a chart showing how frequently coaches throw a challenge flag (x-axis) versus how successful they are on those challenges (y-axis). The data shows all games for each coach dating to 2004, the first year in which the league allowed up to three successful challenges per team.
In the top right of the graph, Green Bay Packers head coach Matt LaFleur has been the most frequent/successful challenger (winning four of seven challenges), though that has come with only ten games worth of data. More robustly, John Harbaugh, Sean Payton, Bruce Arians, Pete Carroll and Doug Marrone stand out as coaches with multiple seasons under their belts who frequently, but relatively accurately, use coaches’ challenges.
Alternatively, among coaches with at least half a challenge per game, only Freddie Kitchens of the Cleveland Browns has yielded a lower success rate than Gruden. And in the top left, Jason Garrett of the Dallas Cowboys boasts a low challenge frequency (0.29 per game), but when he has challenged, he has successfully overturned the decision on the field more than 50 percent of the time (23-for-45).
Among non-active coaches, and using games after 2004, count former Seattle coach Mike Holmgren as one of the most passive challengers (0.28 per game), with Mike Shanahan of the Denver Broncos (0.52 per game) on the more aggressive side. Meanwhile, Jim Caldwell (Indianapolis Colts) won 63 percent of his challenges, compared to 27 percent for Norv Turner.
Visualizing the Special Teams Gunner
NFL special teams players may not be household names, but they play a valuable role in their team’s success. One key special teams player is the punt-team “gunner” – a player who lines up near the sideline, with the goal of moving downfield as fast as possible to eventually tackle the punt returner. The main challenge of being a gunner – there’s usually at least one defender in your way.
"You're trying to beat two guys in what amounts to a running street fight at a sprint,” said Steve Tasker, to Esquire Magazine. Tasker would know – as a seven-time Pro Bowler and five-time first-team All-Pro, he’s considered one of the best gunners in NFL history.
Though it’s difficult to highlight Tasker’s effectiveness today without the help of video, we can use Next Gen Stats to compare and contrast how today’s gunners operate.
For example, we took the player from each team who has played the gunner role most often for his team in 2019 (Weeks 1 to 7). Below is each player's path, standardized so that they are starting at the same line of scrimmage (moving from the bottom of the image to the top), and using only punts in which each gunner was defended by one player only. Players are arranged in order by who covered the most vertical distance on their coverages in the first 4.5 seconds after the punt, with Justin Bethel (formerly with the Baltimore Ravens, now with the New England Patriots) traveling the furthest in the top left, and George Odom (Indianapolis Colts) traveling the shortest in the bottom right.
One aspect that stands out is how while some gunners only line up on certain sides (Ameer Abdullah on the Minnesota Vikings favors the offense’s left side, while the Cleveland Browns’ KhaDarel Hodge lines up on the right, for example), others like New England’s Matthew Slater are equally likely to line up on either side. In the chart, the typical gunner appears on about 25 punt plays.
Although tough to determine in the chart, the effectiveness of certain gunners in moving downfield is interesting. Players in the top row, for example, are about 28 yards downfield, roughly six yards further ahead than players in the bottom row.
Finally, with Bethel recently signing with the Patriots, New England now boasts two of the league’s fastest gunners (Slater is a seven-time Pro Bowler).
Other interesting gunner tidbits?
Matthew Slater, Josh Bellamy (New York Jets) and Brandon Wilson (Cincinnati Bengals) each have been double teamed by defenders 15 times, leading the league.
Against single defenders, no one travels quicker in the play's first two seconds than Johnny Holton Jr. of the Pittsburgh Steelers. At that point, Holton is an average of 7.7 yards downfield, typically about 0.4 yards in front of the next fastest gunner at that point (Slater).
When facing two defenders, Dee Virgin (Detroit Lions) and Trenton Cannon (New York Jets) are the only two gunners to average being at least 20 yards downfield at the 4.5-second mark after a snap.
The Miami Dolphins lead the league in the rate with which they double team at least one gunner, doing so on 62 percent of punts. Alternatively, the New Orleans Saints put two defenders on a gunner on only 20 percent of opponent punts.
What Numbers Tell Us About Preseason Play Time
One aspect of football that has recently been a point of discussion is the role of preseason games. As of the 1978 season, teams have almost always played four preseason games in each season.
To assess how preseason games may have changed over the past decade or so, our Football Operations data and analytics group analyzed team behavior dealing with the playtime of players who started for their teams during Week 1 of the regular season. When looking at this group of eventual starters, how often did they play in each of the four preseason weeks?
The following chart shows the percent of possible snaps for Week 1 starters in each week of the preseason, as well as Week 1 of the regular season. Averaged across all positions, starters typically play about 80-85 percent of the available snaps in a regular season game, a rate which has mostly stayed consistent over time. This is important, as it suggests that any drop in playtime is not due to coaches using player rotations more often.
Alternatively, playtime in preseason games for Week 1 starters has dropped substantially over the last decade. In the figure, the lines in blue have been trending downwards for several years, reaching all-time lows for playtime in 2019.
Some facts that stood out:
Among preseason Week 3 games (abbreviated as Pre W3 in the chart), Week 1 starters played nearly 50 percent of available snaps in 2010. In 2019, Week 1 starters played 23.6 percent of available snaps.
Week 1 starters only played 1.7 percent of available snaps in preseason Week 4 games in 2019.
The year-over-year drop in preseason participation of Week 1 starters was larger this past year (2019 vs. 2018) than in any previous period over the last decade.
In 2019, there were 85 regular season Week 1 starters that did not play a single snap during the preseason. From 2008 to 2018, the league averaged 24 such players per season.
Altogether, snap time percentages among Week 1 starters dropped to new lows in 2019, information that is valuable as the league assesses the role of preseason games in the context of the larger framework of NFL scheduling.
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.
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.
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.
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.
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: 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.
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.
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.
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.
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:
Start small. Build out your work for one play. Write a function for that one thing and then generalize it to all plays.
Get rid of unnecessary data as soon as possible. Drop columns you aren’t using and rows you don’t need.
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.
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