Using #Tableau to Evaluate Jay Cutler’s Career

Introduction

I am not a football expert, but I am an expert at watching football. I also happen to be able to use Tableau to uncover trends in data. So for this post and a few additional upcoming posts, I decided to combine these two abilities to examine the performance of NFL quarterbacks.

To assess the performance of NFL quarterbacks, I apply a quantitative approach to examine questions such as what makes a player an “elite” quarterback? Who are the “elite” quarterbacks? Who are the most stable and reliable quarterbacks? For certain high-performance quarterbacks that have shown exceptional consistency, I examine their careers using Tableau. These articles are not intended to be biased in any way. My statements will all be supported by data with opinions mostly left to the sportswriters who write about these quarterbacks on a daily basis.


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I am a life-long Chicago Bears fan and I enjoy watching the game. If you don’t believe me, just ask my wife about the zombie mode I achieve during a Bear’s game. I also happen to really like Jay Cutler and I believe that he has gotten more than his fair share of criticism from the Chicago fans. For this reason, I have chosen Jay as my first quarterback subject. I want to understand the truth about Jay’s performance to understand if there is really a “Good Jay” and a “Bad Jay”.

Although I have said that I’ll leave my opinions aside, I do believe that Jay Cutler has all the skills, training and experience to be a very good NFL quarterback, possibly even an “elite” quarterback. Unfortunately for him, he has had three head coaches and six offensive coordinators in a nine-year career. With this professional experience being well documented, we should realize that this lack of system continuity would make it hard for anyone to achieve enough high-performance consistency over time to allow him to reach an “elite” status. Although Jay is continuing to get better at his craft as I will show in this post, coaching upheaval in his career path has hampered his performance. You can put yourself in his position by imagining that every year and a half, you get a new boss that tells you what to do on your job and each one of these bosses has a different philosophy on how to do the job.

To understand Jay’s career, I present his entire record of NFL performance with two key measures including the Total QBR and team winning percentage. The Total QBR is a measure created by ESPN and defined as the following:

  • TOTAL QBR: Total Quarterback Rating, which values the quarterback on all play types on a 0-to-100 scale.

ESPN does not present how this rating is calculated. The ESPN Total QBR formula is a trade secret. But I have a secret of my own.  My buddy and I have cracked this secret code and we will be publishing the formula in an upcoming post, so stay tuned if you like to get nerdy and you like stats.

For every NFL game since 2006, ESPN has calculated the Total QBR for all quarterbacks in every NFL game (including playoffs). Through week 6 of the 2014 NFL season, there are a total of 4,581 quarterback QBRs that have been compiled. If you want to understand this rating system, click this link. The winning percentage I present is simply the number of games won divided by the number of games played for any given quarterback.

Since the Total QBR is a normalized measure on the scale of 0 to 100, it makes it easy for us to rank quarterbacks against one another. Just like when you were a kid in class, there was always that one kid that “blew the curve”, so to speak, by scoring way higher than everyone else. I will say only a couple of things regarding this measure. First, no quarterback has ever rated a 100 in a game. Some players have come close, including Joe Flacco this past weekend (10/12/14) when he scored a 99.7. Secondly, the highest lifetime average QBR (since 2006) for any player with more than 100 games played is 75.6. Can you guess who that is? You will have to read my upcoming posts to get that answer. No other player has even maintained above an average of 70 QBR. This metric really punishes the quarterbacks for less than optimal play.

The Data Used In The Analysis

I spent an entire week this past Saturday, mining this data from the ESPN website. Although it did only take me a few hours, it seemed like a week! That old joke is great when you have to describe some pure drudgery, and mining and manipulating data from websites can be described that way. However, sometimes to tell a story, you have to do the research and it isn’t always fun. Unfortunately for me, I don’t have interns to do the dirty work but I do have another awesome ally in the form of Tableau. So after another few additional hours of data magic, I started the analysis and completed it in a couple of hours. Although I only have about 10 hours in this particular post, there will be many more hours spent on the additional posts in this series.

The Jay Cutler Analysis

Jay is an awesome athlete that possesses superior running and throwing ability as well as a high football knowledge according to his coaches. He is highly intelligent and is capable of dominating football games. The problem is, his career has been described as one of inconsistency. There are fans that hate the “Bad Jay” and love the “Good Jay”. There are all kinds of fan groupings for Jay ranging from awesome supporters to really negative haters. Since I no longer live in Chicago, I only hear about these things once in a while but I know that Jay Cutler has been mentioned a time or two as a derisive athlete in Chicago. Although fans will claim that he deserves the boos he gets when he plays poorly, he holds more Chicago Bears passing records than anyone else in the history of the franchise. The man is unquestionably a very good quarterback (at times, I suppose!) and Chicago fans have seemed to forget about the years of quarterback futility that they endured for decades before Jay came to the team six years ago.

One of the problems Jay has experienced in his career is that his coaches have changed very frequently. He has not had the luxury of a stable offensive system throughout his career. In the following analysis you will see that he had two offensive coordinators in his first three years in Denver followed by four coordinators in six years in Chicago. One of the insights I looked for in this analysis is how has he performed under the guidance of these coordinators. So let the analysis begin.

Figures 1 through 6 show Jay’s performance on a game by game basis for each of the coordinators. The dashboard shows if the game was won or lost and the QBR rating for the game. Also shown is Jay’s average QBR rating achieved during winning games, losing games, and the overall QBR for the entire time he played for that coordinator. For example as shown in Figure 1, when Jay played for Rick Dennison, he achieved an average QBR of 68.6 for winning games, 39.75 for losing games, and a rating of 48.77 for the eight-game moving average chart.

Figure 1 also shows that Jay won 45% of the games he played when Dennison was the coordinator. It is clear from the difference between the winning game QBR and the losing game QBR ratings that Jay’s performance is directly related to the team’s ability to win the game. If Jay played poorly (lower QBR), chances were good that his team lost the game and so his lower average QBR rating of 39.75 reflects his struggles during losing games.

The difference in average QBR between winning games and losing games is a key indicator of quarterback consistency.The smaller difference there is between these measures, the more consistent the quarterback played. Of course, losing a game is possible for a number of reasons and the quarterback isn’t responsible for all these reasons. Therefore, the QBR to winning percentage relationship isn’t a perfect measure but you will see a more rigorous analysis of this shown later in this post.

Figure 1 - Offensive coordinator Dennison in Denver.

Figure 1 – Offensive coordinator Dennison in Denver.

 

Figure 2 - Offensive coordinator Shanahan in Denver.

Figure 2 – Offensive coordinator Shanahan in Denver.

 

Cutler_Turner

Figure 3 – Offensive coordinator Ron Turner in Chicago.

 

Cutler_Martz

Figure 4 – Offensive coordinator Mike Martz in Chicago.

 

Figure 5 - Offensive coordinator Mike Tice in Chicago.

Figure 5 – Offensive coordinator Mike Tice in Chicago.

 

Cutler_Kromer

Figure 6 – Offensive coordinator Aaron Kromer in Chicago.

 

Click here if you want to download the Tableau Public workbook for this example to investigate the Jay Cutler career for yourself. The interactive dashboards shown in Figures 1 – 6 have hyperlinks to the stats for every game that Jay has played as shown in Figure 7. You just click the little green or red circle that has a week number shown on it and the link will take you to the game. This allows you to see how he played in any of his 100+ games.

game_details

Figure 7 – The blue hyperlinks in the Tableau tool tips can be chosen to see Jay’s profile or his game stats for any game you choose.

Clearly, Figures 1 through 6 shown that Jay was more successful with some coordinators than with others. Figure 8 shows how his QBR distributions have changed through the years. At the bottom of the chart you see the name of the coordinator for that season. When time (each year) is shown as a variable as in Figure 8, Jay’s performance seems a little erratic and hard to explain. For example in 2010 he had a series of very high performing games but it was also coupled with some poor games, such that the span of his performance was very wide. Under the same coordinator (Mike Martz) in 2011, his median QBR took a huge dive from 70.3 to 42.3. When something like that happens, a new coordinator is called in to improve the quarterback play, and that is exactly what happened when Mike Tice arrived on the scene in 2012. To remove some of this variation, I decided to remove time from the plot and investigate Jay’s performance relative only to the coordinator.

Offensive_Coordinator

Figure 8 – Annual QBR distributions shown in a box and whisker plot format.

Figure 9 is the same type of chart as Figure 8 but the time aspect has been removed so that the composite performance that Jay has achieved can be see for each coordinator. It is clear that Jay improved in each of his two “coordinator time periods” in Denver and has improved in each of his four “coordinator time periods” while in Chicago. Jay is now playing his best football under the direction of Aaron Kromer and it is easy to see from previous Figure 6 that the difference in his average QBR between winning and losing games is shrinking. This means that Jay’s consistency is improving with Kromer as the offensive coordinator.

Figure 9 – A comparison of Jay’s performance for each offensive coordinator.

 

Jay’s consistency, or lack of consistency is shown in Figures 8 and 9. Jay is most inconsistent when the span of the QBR data is wide. Consistent quarterbacks will have less variation than shown in Figures 8 and 9. Since I was able to see such a large variation over time for Jay, I wondered if this inconsistency could be related to time of the season. When you look at this fluctuation on a game by game basis, there is too much noise to discern a trend in his performance.

The ex-Bear coach Lovie Smith was famous for breaking the sixteen-game season down into quarters. Since he knows a lot more about football than I do, I thought it would be a great idea to try that with Jay’s data. The first quarter corresponds to games in September, the second quarter to games in October, etc.

As shown in Figure 10, Jay has shown a tendency to have two good quarters (Sept and Nov) but he falters in October and December. These trends occurred both in Denver and Chicago. The unfortunate thing for Jay is that he is not ending the season on an uptick. He is flat in December, with less than a 50% winning percentage when the games mean the most. This is why he is not taking his team into the playoffs. Playoff caliber teams have hot quarterbacks in the last month of the season. Due to parity in the NFL, teams that get hot in December can carry that momentum to Super Bowl victories, as has already happened many times.

 

Good_Jay_Bad_Jay

Figure 10 – The four quarter breakdown of the season to see when Jay is a high-performer and a less than optimal performer.

If I were a coach on the Bears, I would try to figure out what it is about Jay that is causing his performance to fluctuate so much during a season. In particular, it is important to understand why he struggles in October and December. December might be due to weather, but what is the problem in October? Maybe it has to do with his own injuries or the injuries of his teammates. If the Bears are going to be a playoff team in the next few years, they need to get Cutler to play at a high level in December. That is key to the future success of the franchise.

Finally, I looked at how Jay’s QBR rating translates into his teams winning football games. Figures 11 (Denver) and 12 (Chicago) show scatter plots of monthly winning percentage versus the Total QBR that Jay earned during each month. The r-squared for these two charts are 32% and 36.6%, which indicates that Jay’s performance is very important when it comes to winning football games.

Wins_vs_QBR_Den

Figure 11 – Winning percentage vs Monthly Total QBR while Jay played in Denver.

Wins_vs_QBR_Chi

Figure 12 – Winning percentage vs Monthly Total QBR while Jay played in Chicago.

As shown in Figure 12, Cutler is now producing high QBRs but this isn’t translating into a higher winning percentage. For this season and last, the reason for that has to do with the complete failure of the Chicago defense in 2013 and the inconsistency shown in 2014. Cutler has been responsible for one of the losses this year, but most of the blame should be placed on the defense.

To conclude this analysis, I suggest that the Chicago fans should give Cutler a break and look at his improvement over time. It is pronounced and apparent in this analysis. The man is good. Appreciate what you have while you have it. If you have a short memory, go back and see how many quarterbacks the Bears had before Jay arrived. Remember the inconsistent play that plagued your team through the years. Once the defense gels, the Bears will be able to go deep into the playoffs. Good things are on the horizon in Chicago especially with the offensive-minded head coach Mark Trestman becoming more acclimated with life in the NFL. The combination of Trestman, Kromer and Cutler will produce great offensive football over the next few years for the Bears.

Upcoming in Part 2 of this Series

Finally, also remember what Brian Urlacher once said: “As long as Green Bay has Number 12 under center, the Packers will be a force.” In part 2 of this series, I will be looking specifically at the performance history of Aaron Rogers to see how he has achieved his greatness.

Update 11/17/15

Another year has passed since I first wrote this.  Here is a link to an article today that discusses Jay’s performance in 2015. I wonder if Chicago fans are starting to see the truth about Jay Cutler. As I said last year, he is emerging as an elite quarterback. The visual analytics I presented in this article indicated his improvement and now the data is being generated that supports my observations. I should take the time to update his Total QBR’s in this article.

Since missing a Week 3 loss in Seattle with a hamstring injury, Cutler has led the Bears to a 4-2 record by passing for 1,700 yards with 11 TDs, three interceptions and a 98.7 rating.

Cutler has not thrown multiple interceptions in a contest this season, an eight-game stretch that is the longest of his 10-year NFL career. His passer ratings over the last six games have been 89.4, 88.4, 88.8, 94.4, 100.5 and 151.0.

Update 1/8/16

According to ESPN, Cutler jumped 6 spots from 2014 to 2015 in total QBR. Here is the article: Cutler improves Total QBR in 2015.

2 thoughts on “Using #Tableau to Evaluate Jay Cutler’s Career

  1. Hi there, I just read and went through your post and I have to say it was awesome. I am currently attempting to figure out how to use Tableau for sports data analysis. Might you have any tips for me on ways I can make the process useful? I am 100% new to using Tableau. Thank for this post.

    • Hi Aaron,

      Thank you for your kind words. I have many thoughts about using Tableau with sports data. My primary issue is not having enough time for exploring what is possible. Since you are new to Tableau, you will have to learn some basics, some important fundamentals to be able to create interesting and informative dashboards. I’d recommend reading “Learning Tableau” by Joshua Milligan (@VizPainter on Twitter). Joshua’s book provides all that you need to come up to speed quickly with Tableau.

      You can use my most recent sports article (https://3danim8.wordpress.com/2015/07/24/this-is-my-gift-to-all-fantasyfootball-players/) to get started visualizing quarterback play using the TotalQBR data set I provide in that article. That will be good training material for you.

      Good luck and let me what if you need any help,

      Ken

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