Advanced NFL Stats

Exploring the Causes of a Sack Pt. 1

2013-05-13T07:00:00.000-04:00

A guest post by David Giller. Born and raised in Swampscott, MA, David attended Vanderbilt University where he was the starting longsnapper for the Commodores. He graduated summa cum laude with a degree in economics/corporate finance. David currently works as a business analyst for a Bain Capital Ventures portfolio company.

I would first like to thank Brian for his suggestion to post my study here in an effort to spark some interesting conversation and obtain some valuable takeaways. My post contains the results of a recent study I put together which focuses on the causes of sacks in NFL games. Although it is fairly detailed, I believe there are still areas of further development, some of which have been explored in an appendix to this initial study and will be coming in the second installment of this post.

As a disclaimer, the number of sacks were provided from an official source; however, the timing of the sacks, count of offensive blockers/defensive rushers was determined from my individual film study.The full piece is attached in a link, however, I have included some highlights below.
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Point / Counterpoint on Rodgers' Extension

2013-05-07T07:00:00.000-04:00

Today we're going to try a new format here at ANS--a debate between me and myself on the market value of Aaron Rodgers' recent contract extension. Rodgers recently signed a deal adding 5 years to his current contract. This will pay him roughly $21M per season over the next 3 years. See if you can figure out which Brian has the right idea and why they get different results.

Brian 1: Rodgers' new deal is a fantastic bargain. He's one of the truly elite QBs in the league today, and guys like that don't grow on trees. But more scientifically, just look at this super scatterplot I made of all veteran/free-agent QBs. The chart plots Expected Points Added (EPA) per Game versus adjusted salary cap hit. Both measures are averaged over the veteran periods of each player's contracts. I added an Ordinary Least Squares (OLS) best-fit regression line to illustrate my point (r=0.46, p=0.002).

Rodgers' production, measured by his career average Expected Points Added (EPA) per game is far higher than the trend line says would be worth his $21M/yr cost. The vertical distance between his new contract numbers, $21M/yr and about 11 EPA/G illustrates the surplus performance the Packers will likely get from Rodgers.

(This plot includes for all free-agent or veteran extensions since 2006. Cap figures are averaged for each player's career and, to account for cap inflation, are adjusted for overall league cap ceiling by season. Only seasons with 7 or more starts were included.)

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How Much Money is a Sack Worth?

2013-04-25T11:22:00.000-04:00

I realize it's draft season but I'm still working on building a salary database and combining it with performance statistics. Most of the work in this type of analysis is building the data, but it's harder than it might seem at first. Salary data might use one kind of player identifier while performance statistics use another. Merging them and creating a flat data file to play with is more than half the battle. There's still more I need to do. First up will be segregating free agent years from rookie contract years. But for now, here's some trivia.

Offenses get all the attention, so I thought I'd toy around with what I have so far on defense. Sacks are one of the most visible and tangible defensive statistics. I took the primary sack makers, defined as DTs, DEs or LBs who have averaged over one sack per season in their career, and plotted their career average sack rate against their average cap hit.

A few notes about the data: Only those years with cap hits greater than $1M were included as a crude way to focus on every-down starters. Additionally, only seasons where the player had 7 or more game appearances were included. The data ranges back as far as 2006 for whoever I had salary data for. Cap hit was adjusted for salary cap inflation--All cap hits are in $2012 cap dollars. Lastly, 'sacks per season' is extrapolated for each player to full 16-game seasons.
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Cade Massey on Flipping Coins and the NFL Draft

2013-04-22T07:00:00.000-04:00

Readers of this site will recall the name Case Massey. Along with fellow noted economist (and paternalist) Richard Thaler, he co-authored the Massey-Thaler draft study titled The Loser's Curse. The paper found that, under the previous CBA, "surplus" draft value peaked with picks in the late first round and early second round. Surplus value was defined as the expected performance value above which a team could expect by spending an equivalent amount on a veteran free agent.

Massey has continued research the draft. His presentation at the 2012 MIT Sloan Sports Analytics Conference outlines his recent findings. (I recommend using IE to view the presentation. Chrome didn't play nice with the video.) The slides from the brief can be viewed here.

If I understand things correctly, Massey has found that:
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EPA Production and Cap Value, Skill Positions

2013-04-18T07:00:00.000-04:00

I've been playing around with the connection between player value and production, and I thought I'd post some interesting observations.

As a measure of production I used Expected Points Added (EPA)--actually EPA per game to account for injury shortened seasons. For the measure of player value, I used cap hit. Cap value is useful because it boils down the complexity of many NFL contracts into one number. It can be tricky, though, as many contracts can be quite uneven from year to year in terms of cap value. For cap management and player-incentive purposes a 4-yr/$40M will often diverge far from a steady $10M per year cap hit. To account for this, I averaged each players' per year cap hit for the full period ('06-'12) and plotted against each player's EPA/G. The purpose of doing this is to see what level of production teams expect per $1M of salary. Is there a solid connection between true production and salary?

There are assumptions and limitations inherent in this analysis. Player production is dependent on the abilities of their teammates. WRs and TEs rely on their QBs to get them the ball, and some will have better passers and some will have worse, and vice versa. RBs are dependent on their line and scheme. But over the league as a whole, these considerations would (ideally) balance out. Put simply, for every Larry Fitzgerald being victimized by the offense around him, there's a Brandon Lloyd benefitting from being on a great offense.

The chart below plots offense skill position production by annual average cap hit. Each position is color coded.
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Sloan Sports Analytics Conference Video

2013-04-08T09:59:00.001-04:00

The video of the panel I participated in at the MIT Sloan Sports Analytics Conference has been posted. The panel consisted of Herm Edwards, Jack Del Rio, Thomas Dimitroff, and myself. Tony Reali was the host and made it one of the more entertaining panels at the conference.

The format kept the event from becoming a dry drone fest. We took several interesting in-game decisions and analyzed them from our own perspectives. Also, Herm was wearing really colorful socks. If you want to know why there is a picture of an air conditioner in this post, you'll have to watch the panel.

As a bonus, here is the link to the other football panel at this year's conference, featuring FO's Aaron Schatz, Paraag Marathe (SF), Kevin Demoff (STL), and Scott Pioli (formerly KC). The videos of the other conference panels can be found here.

Thanks to Ben Levitt, Monte McNair, Daryl Morey, and the other organizers who made the event so much fun.

SITE NEWS

2013-04-01T05:00:00.000-04:00

Many readers have probably wondered why I haven't been posting articles nearly as often as I had in years past. Here's the reason--The one-year news embargo is officially lifted, and I can officially announce my participation in a project so big and so cool I can hardly describe it with words. I'll let the page at IMDb say it all. Thanks to everyone for all their support throughout this process. It wouldn't have been possible without the loyal fans of ANS!

Win Probability Forfeited 2012

2013-03-18T07:00:00.000-04:00

by Matt Meiselman

Matt has been helping me crunch some 4th down numbers this off-season. He is a senior at the University of Maryland studying broadcast journalism. He's originally from New Jersey, but loves New York sports. Matt aspires to work in sports media and has a passion for sports statistics. -BB

Fourth down decision making is one of the most controversial aspects of coaching in the NFL. Act too aggressively and miss? You get blamed for a loss. Act too timid? The fans will be calling for your head. Most NFL coaches are operating with the mindset that whatever puts their team (and their job) at the least amount of risk is the right choice to make. This has been, and will always be, the wrong way to try to win a football game.

In last year's article on this subject, Brian talked about how coaches aren’t just saving wins if they’re more aggressive; they are simultaneously forfeiting wins by being too meek. In 2011, the average team forfeited .65 wins for the year on 4th down decisions alone. The NFL has started to become more risk-friendly instead of risk-averse, and you’d expect that with more innovative minds in the game, like Bill Belicheck and Jim Harbaugh, the league would be trending towards more optimum game management. This was not the case in 2012.

During the 2012 season, the average team forfeited .73 wins, a significant increase from the year before. The average win probability forfeited per opportunity also rose, jumping from 1.6% of a win to 1.9% of a win. Below are the calculations for each team:
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Team Analytics Interns

2013-03-14T21:55:00.001-04:00

Want a chance to work for an NFL team?

An NFL team is looking for football research interns for the summer. There is a chance that the position could become full time after the summer if the candidate is outstanding.

All interested candidates should send a resume and cover letter to nflanalyticsintern@gmail.com. In the cover letter, please detail any relevant analytics experience and describe one question you would like to study if you had access to an NFL team’s data. Special preference will be given to those with database and/or programming skills.

Tell 'em ANS sent you.

What is Ed Reed's Going Value?

2013-03-11T08:00:00.000-04:00

Last year I looked at the salaries of free agent safeties to estimate the going rate based on production stats. FA safety salary correlated fairly well with production, specifically +EPA (positive Expected Points Added). With Ed Reed, one of my favorite safeties, on the FA market at moment, I thought I'd take a look at what would be a fair market price for his services in 2013.

Here, price is defined by salary cap hit. NFL salaries are notoriously complex with bonuses and guarantees. But these are boiled down into a single cap number, which is the cost to his team's overall salary cap, the most precious resource it has in acquiring and keeping players.

The analysis here produced the chart below. In short, top FA safeties are valued at about $2.0 million for every +EPA per game, minus $170k.

For example, for a guy who produces 2.1 EPA/G, his going rate would be about $4M per year. So where would Ed Reed stand in this context?

Reed is a future Hall of Famer, but he is now 34 and headed into his twelfth season. Here is how his total production and per-game production have tracked over the course of his career. (Note that all stats are for regular seasons only. +EPA stats can be found here.)
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New NFL.com Stat Viz

2013-02-26T13:39:00.000-05:00

The NFL’s website is getting on the data visualization bandwagon. They recently unveiled a project called ‘StatLab’ which involves two types of plots.

The first one isn’t terribly interesting. It just illustrates each division in terms of W-L record. The more wins, the bigger each team’s circle. This is a textbook lesson is when a table is better than a chart, but it’s a start. How about spicing it up with a dynamic chart that recreates the horserace of the season? Teams grow and shrink as the weeks click by. Or year-to-year improvement/decline in season records?

The other set of charts is more interesting and interactive. There are ‘radar’ charts for player statistics. Each stat is depicted as a ‘dimension’ or distance from the center of a plot. The better the stat, the further out the point. The points for each stat are connected and filled, so the bigger the shape, the better the performance. You can select a number of players to compare. The average stats are depicted for the selected position too. (Don’t show this to Joe Flacco’s agent. It’s amazing how close Flacco’s numbers hew to average in nearly every category. The one exception is interception total.)

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Sloan Sports Analytics Conference Update

2013-02-25T10:30:00.000-05:00

Sloan is coming up this Friday and Saturday. I'll be part of panel consisting of Jack Del Rio, Herm Edwards, and Thomas Dmitroff. The title of the panel is Monday Morning Quarterback: Coaching and In-Game Decisions. The format will primarily involve looking at a variety of interesting situations from actual games. Tony Reali from ESPN will be the moderator, and it goes without saying that it should be fun. It's on Saturday afternoon, and is the keynote feature event of the entire conference. ...Ok, I made that last part up...It's actually on Saturday morning. If you won't be there, the panel will be featured via webcast.

If you're going to be at Sloan, please come say hi. (Please note that I'm not the former GM of the Toronto Maple Leafs.) Don't let my unapproachable scowl fool you. It's my first time there, and I'm looking forward to meeting as many like-minded analytics fans as I can.
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Joe Flacco's Playoff Run Was Truly Elite (And Colin Kaepernick Was Pretty Good Too)

2013-02-06T09:00:00.000-05:00

In January of 1990 -- the 1989 playoffs -- Joe Montana rattled off three straight incredible games. The 49ers legend compiled 800 yards on 65-of-83 passing (78 percent), fired 11 touchdowns and didn't throw a single interception. His five touchdowns and 297 yards paced San Francisco to a 55-10 demolition of John Elway's Denver Broncos in Super Bowl XXIV. The performance capped what remains the best postseason ever from a quarterback.

Joe Flacco's January and February of 2013 didn't eclipse Montana's accomplishments, but he arguably came as close as anybody in the 23 years since, and almost certainly closer than any other Super Bowl winner. Flacco did it in one extra game and 43 extra pass attempts, but he matched Montana's playoff record 11 touchdowns (also shared with Kurt Warner in the 2008 season) and zero interceptions. His 1,140 yards rank third all-time, just behind Warner and Eli Manning in last year's playoffs.

We only have WPA and EPA data dating back to the 2000 season, but Flacco's playoff run ranks second in both to Warner, and tops of any Super Bowl winner:

Some notable performances are highlighted; mousing over a dot will reveal the quarterback's identity.

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More SB 47 Analysis

2013-02-04T21:40:00.001-05:00

I was just getting started with a more full analysis of the SB game decisions. I wanted to take a detailed look at BAL's two FG decisions and SF's decision not to go for 2 when down 28-13 (28-12 prior to the XP). Just when I finished doing the calculations for BAL's 4th and 2 from the SF 20 and their 4th and goal from the SF 1, I got an email from intrepid reader-correspondent Matt Meiselman.

Matt kindly sent his own analysis, using the 4th down and win probability calculators. This is exactly what those calculators are for...so I don't have to do all the work! Take it away, Matt:

In a Super Bowl with colossal storylines, including the impending retirement of Ray Lewis and a 34-minute blackout delay, the battle of the Harbaughs was arguably the biggest. Both coaches are considered among the league’s elite, and there won’t be much criticism of either of them following such a well-played season by both the Ravens and 49ers. In the Super Bowl, however, there were a few decisions made by the Harbaugh brothers on 4th down that significantly decreased their respective teams’ chances of winning.

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Slate/Deadspin: The Little Things that Cost the 49ers

2013-02-04T17:43:00.001-05:00

My analysis for SB47 is up over at Deadspin and Slate. I took a look at some of the more interesting decisions from the game--the fake FG, the intentional safety, and a couple 4th downs. But the biggest decisions in the game turned out to be the littlest ones. If there were ever a case study in the importance of saving your 2nd half timeouts, this game should be it.

Live Super Bowl Win Probability and Chat

2013-02-03T18:15:00.002-05:00

Join the crowd and follow the in-game win probability here. Throw in your two cents with the chat/comment feature.

2012 ANS Awards

2013-02-02T06:00:00.000-05:00

I'm going to come at these year-end awards from a slightly different angle. The modern NFL has made the QB position so singularly important that any honest, realistic award for MVP would simply go to the top QB of the season. Not only is that boring; it also says nothing about the QBs themselves--just about how central the position has become.

As always, WPA and EPA will be my measure of performance, rather than yard or sack totals. I'm going to look at how each player stacks up against their peers at the same position. The idea is a little like the concept of the replacement player. The most valuable player would be the one that offers the most value above the next best alternative. Still, because the effect of the passing game is so magnified in today's game, this would always be the number one QB. So let's put each position on the same level and see who most stands out above the pack.

Rather than stare at an eye chart of numbers, I made a visualization that plots the top 32 performers at each position. Each player's total WPA is plot on the vertical axis and total EPA is plot on the horizontal axis. I'm not looking at over- or under-performance like when we were looking at 'clutch' play. In this analysis, I'm just looking for the players who show up at the top right of the plot. All numbers are from the regular season only.

There are two additional tabs that simplify things a bit by ranking each player by WPA and by EPA.

And because it's easy and fun, I've included past years too. You can even select 'all years' and see the top 32 at each position since 2000. The WPA Rank and EPA Rank bar charts are especially cool because they identify performance on multiple teams according to team color.
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Regular vs. Post-Season QB WPA and EPA

2013-02-01T15:35:00.001-05:00

As follow up to the recent post about QB 'clutch' performance in the playoffs, here is a comparison of QB performance in the regular season compared to the post-season. I've created a viz to see how each QB season stacks up. Each data point is a QB-year. The x-axis represents each QB's WPA per play in a regular season and the y-axis represents his WPA/P in the corresponding post-season. The trend line shows that there is, as expected, a systematic connection. Winning QBs in the regular season tend to continue to be winning QBs in the post-season. The residual, or vertical distance above the trend line, represents how much each QB over or under-performed in the post-season. I limited the cases to QBs who had at least 25 attempts in a post-season.

For example, Kerry Collins' 2002 post-season stands out in that he dramatically out-played his regular-season self. (It was only one game, but Collins was absolutely heroic in what was one of the most heart-breaking miscues in playoff history. With only a minute left Collins drove the Giants to the SF 23. A botched snap cost New York the game.)

On the other side of things was Tony Romo's 2006 loss to Seattle. Again, a bobbled snap cost the (anti-) hero the game. (Special teams plays are usually excluded in these kinds of analyses, but the play was technically a Romo run.)

Note that Joe Flacco's current playoff run ranks as the eighth biggest over-performing post-season (so far).
Hover over each point to reveal the QB-year. You might want to make use of the zoom controls.
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Super Bowl Game Probability and Voodoo Analysis

2013-01-31T10:57:00.002-05:00

The game probability for the Super Bowl is up at the New York Times' Fifth Down.

This week I talk about momentum and 'voodoo' analysis:

A boulder rolls down a hill and gains momentum. A spark sets a fire, and soon it has built into a blaze. The rains come and soon the river is rushing over its banks. Momentum is everywhere in nature, but applying it to abstractions like team win-loss records in a relatively small sample of football games is what I call voodoo analysis.

Voodoo analysis is the application of apparently intuitive patterns beyond their natural settings. A football team is not a boulder rolling down a hill. It’s not a river bursting through a damn. It’s not a spreading fire. Our brains are continuously looking for patterns like these, and often see them even when they’re not there. That’s why we’re better off taking a disciplined look at the numbers from the full season.

Super Bowl Statistical Matchups

2013-01-29T16:55:00.000-05:00

Here is your one-stop shop for all things advanced stats for SB 47. Team efficiency stats. Advanced team stats. Top individual performers from each team.

Clutch Persistence?

2013-01-29T07:00:00.000-05:00

I recently wrote about clutch QB performance in the post-season. This post will take a look at clutch QB performance in the regular season and how well it persists from year to year. The approach is to compare how well a QB performs in high-leverage situations to how well he performs without respect to leverage. To do this, we can compare his Expected Points Added (EPA) to his Win Probability Added (WPA). This involves computing an "expected" WPA for each QB's season based on his EPA. The difference between a QB's actual WPA and his expected WPA could be considered his "clutch-WPA."

Here is a graphical depiction of what I'm talking about. This chart is from a 2010 article on clutch play. The vertical distance between a QB's expected WPA and his actual WPA, shown as the red line below, is clutch-WPA.

For this analysis, I used per-play metrics: EPA per play, WPA per play, and clutch-WPA per play. Only QB seasons with greater than 200 pass attempts were included.

To estimate "persistence" I measured the year-to-year correlation in our three variables of interest. The idea is that the stronger the correlation, the more persistent the measure is as a quality of that QB. If there is no year-to-year correlation, then the variable may only be random.

The table below lists QB year-pairs and their correlation in our three variables of interest. The 'n' column lists the number of year-pairs in the analysis. For example, the 1 - 2 row represents the 90 cases of first and second seasons each QB appears in the database. The database begins in 2000, so year 1 does not necessarily represent a QB's rookie season or his initial season with 200 attempts. (Note that both seasons in each year pair had > 200 pass attempts.)
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Clubs Stay Conservative In No Man's Land

2013-01-28T08:00:00.000-05:00

Initially, I was surprised Bill Belichick elected to punt last Sunday on a 4th-and-8 from Baltimore's 34-yard-line. Belichick's Patriots were up by six, and Belichick of all coaches should understand a six-point lead in today's NFL is far from safe. The decision was far from a no-brainer, as Keith sketched out last week.

But those calculations use generic odds; Belichick had his staff's play calling and Tom Brady's arm on his side. As well as his defense had played in the first half, it was still the 29th-ranked squad by efficiency entering the game. Joe Flacco and the Ravens offense, although mediocre overall, showed an ability to pick up large plots of yardage in just a few plays. The difference between handing the Ravens the ball at their 34 or around their 10 (Baltimore fair caught the eventual punt at the 13) seemed worth risking for the Patriots in order to maintain possession.

Of course, the Ravens proceeded to drive 87 yards as Joe Flacco dissected New England with a 13-play no-huddle masterpiece of a drive. Baltimore took a 14-13 lead and wouldn't relinquish it.

Belichick is a risk-taker, indeed -- even if he softens up between now and his retirement (assuming whatever form he takes on this earth is subject to human aging), his famous decision to go for it on a 4th-and-2 in his own territory against Indianapolis in 2009 cemented that legacy. If Belichick wouldn't try in that situation -- one we suggest is a toss-up, slightly favoring a conversion attempt -- who would?

Unsurprisingly, attempting to convert a 4th-and-8 between the 30 and 40 outside of the fourth quarter isn't just outside the limits of Belichick's courage. NFL teams faced the situation 15 times this season (including playoffs) before Belichick faced it last Sunday, and 15 times the teams kicked -- four punts and 11 field goals, with just six successful.

4th-and-8 happens to be a convenient example -- teams at least tried once on everything else from 4th-and-1 through 4th-and-11. But coaches shy away from attempting any fourth down longer than one yard, and anything longer than three is treated like the plague (looking at just the first three quarters to try and eliminate desperation attempts).

Coaches tried for the first down over 75 percent of the time on 4th-and-1, but an extra yard scared away half those brave souls. For some reason, teams almost never attempted with between six and nine yards to go, but were more willing to risk the fourth down attempt (although I would suggest punting is a risk as well, as the Patriots found out) with 10 or 11 to go.

Here are the plays separated out into individual blocks, with wide blocks representing successes and narrow ones representing failures. You can mouse over the individual blocks for a description of the play:

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Who's the Clutchiest Post-Season QB?

2013-01-27T22:55:00.000-05:00

What does clutch even mean? To me clutch means someone who has over-performed his typical expected level of performance in high-leverage situations. A great QB who plays equally well in clutch situations as he does in other situations isn't 'clutch' to me. But a guy who raises his game in high-leverage situations can be called clutch.

The ability to over-perform in clutch situations as a persistent skill almost certainly does not exist. (More on this in a future post on year-to-year correlations in general performance levels and clutch performance levels.) But that's not to say that some players' better moments happened to have occurred when things mattered the most. Although clutch as a quality or skill does not exist, clutch as an event certainly does.

Trying to define clutch is a tricky business. It's an arbitrary exercise with no one correct answer. Should we count situations where a team is down one score or two? With 5 minutes left to play? 4 minutes left? The final 10 minutes of a game?

My solution is to compare a player's Expected Points Added (EPA) with his Win Probability Added (WPA). EPA measures total production without respect to time and score. In contrast, WPA is heavily weighted by game situation. Players whose WPA exceeds what we'd expect based on their EPA could be thought of as clutch, and players whose WPA is below what we'd expect could be considered anti-clutch.

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Patriots Primarily Punt on Fourth Down

2013-01-20T23:35:00.000-05:00

Bill Belichick is known for being one of the greatest football minds in NFL history. He's also known for being one of the "riskiest" play-callers -- riskiest in quotes to emphasize that he actually plays to the odds rather than most of the conservative football minds. Down 28-13 in the AFC Championship game, avid Patriots fan Bill Simmons put it best: "Ravens playing to win, Pats playing not to lose."

Belichick faced eight fourth downs in the game against the Ravens, seven of which were legitimate questions for the best course of action: Go for it, punt or kick the field goal. Whereas we would normally expect Belichick to be aggressive, he seemed more reserved in his decision-making. There are a ton of factors that could explain his passive play-calling. For example, it was extremely windy making field goals more difficult and maybe Belichick did not have faith that Joe Flacco could sustain a 90-yard drive due to the Ravens boom-or-bust offense (the Ravens ended up with three scoring drives of 10+ plays including a 90-yarder and 87-yarder).

Let's look at each fourth down decision, starting with the very first. Remember in the beginning of the game, especially if the score is fairly close, we should look at expected points, but as score and time become bigger factors, we will switch to win probability. Also, note that these are league baselines. The fact that the Patriots offense is No. 1 in the league and far above league average would indicate a higher success rate on 4th-down conversion attempts.

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Feature Enhancement: Time Calculator

2013-01-20T15:09:00.000-05:00

The Time Calculator is a tool that will estimate the time remaining in a game that a trailing defense can expect to get the ball back if they force a stop. It considers the current time and timeouts remaining while factoring in stoppage from the two minute warning and change of possession.

The previous version of the Time Calculator could only base its estimate beginning with the time of the first down snap of a series. For the vast majority of situations that's ok, because offenses will typically only run plays that avoid stopping the clock--runs that stay in bounds. But sometimes there is a stoppage, due to either an incomplete pass,a runner going out of bounds, penalty, or other reason.

The old calculator could account for an unexpected stoppage if you add a notional timeout to the game state. For example, say the defense began the series with 1 timeout, then used it following 1st down, and there was an unexpected stoppage after second down. This scenario would be no different than if the defense began the series with 2 timeouts rather than their actual 1.

Still, it would be easier and more straightforward to make the calculator work for any down. Now you can enter the time at the snap of any down in a series along with the number of timeouts remaining, and the calculator will estimate the time after the change of possession.

All the other options remain the same: the average duration of each play, the game-clock duration between plays, and whether the defense would prefer to trade away some time on the clock to preserve a timeout for use on offense.

Try it out. The Advanced NFL Stats Time Calculator.