By: Edward Egros

cowboys

Biggest Snubs for the Cowboys' Ring of Honor

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There was a time when virtually the only way a Dallas Cowboy could make the Ring of Honor was to win a Super Bowl. All but two current members had at least one championship (Don Meredith and Don Perkins). However, this week Cowboys owner Jerry Jones affirmed Tony Romo would be inducted into the Ring of Honor. Not only did the former quarterback fail to reach a Super Bowl, he would be the first Cowboy in franchise history to be inducted without even having won a conference title.

Individually, Romo may not have had stellar a career as Ring of Honorees Troy Aikman or Roger Staubach, but he does surpass the efforts of Meredith, and for being a part of the Cowboys for a dozen years, he likely deserve a place in north Texas immortality. By including Romo, the Cowboys introduce the idea that championships should not be weighted as much when determining who belongs, perhaps opening the door for others.

This idea leads to a question: Who is the most deserving Dallas Cowboy for the Ring of Honor who has yet to make it? One way to evaluate individual performances is with
Approximate Value by Pro Football Reference. The top eight players in Cowboys history have already been inducted, from Emmitt Smith (#1) to Staubach (#8).

The highest Approximate Value not to have his name on the ring is Cornell Green, a cornerback who played for 13 seasons, including for the 1971 Super Bowl team. With 34 interceptions, 171 games started and five Pro Bowl invitations, Green has a better case to make it than anyone else not there, per this metric. His 9th best Approximate Value is better than Aikman, Romo, Lee Roy Jordan, Larry Allen, et al.

Two other players who finish in the Top 20 but who are not in the Ring Honor include Ralph Neely, a left tackle as part of the '71 Super Bowl champions and Nate Newton, the left guard who played during the Cowboys dynasty of the 1990's. While this metric may not be the perfect way to compare players, it does highlight some inconsistency for why some players have already been inducted and why others have had to wait.

2018 Cowboys Postgame Reports

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For the third-straight year, after every Dallas Cowboys game, I will provide an analytical graphic to begin the conversation as to why the Cowboys won or lost that particular game. However, this year features a new look and simplified visualizations so it's easier to follow and compare what happened. Our graphic is an example from the Cowboys preseason game against the Cardinals.

There are four factors:

- Turnover Margin
- Scoring Efficiency
- Net Yards/Pass Attempt
- Game Control

Our intelligent readers already know what Turnover Margin is, so we move on to Scoring Efficiency, which is essentially points divided by yards. Here, we include percentages, so the more efficient team earns the 100% margin, and the less efficient team shows the fraction of its efficiency compared with its opponent.

Net Yards/Pass Attempt is (passing yards - sack yards) / (passing attempts + times sacked). Because of the reliability of this metric not just to evaluate quarterback performance but also its consistency over time, this serves as an important metric to include.

Lastly, Game Control is based upon a regression where each explanatory variable is the number of rushing yards per quarter and the dependent variable is the likelihood of winning. My research found, predictably, that rushing yards in later quarters matter more to winning than earlier in games. Here, we add up each team's rushing yards and multiply by a factor for each quarter they were rushed in. We then take those results as a proportion to see how much each team controlled the game.

As always, feedback is appreciated!

A New Explanation of Cowboys Graphics

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For the second-straight year, after every Dallas Cowboys game, I will post a recap of the game with an analytic visualization. Once again, these metrics sum up all of the important factors that determine the outcome of a football game. Some of the metrics are the same, while others are more refined and better reflect certain concepts.

Going from the top and working down, once again I will chart turnovers, one of the more impactful statistics in the game. The numbers reflect the turnover margin and the bars reflect how many turnovers were committed.

The next box will look at how the quarterbacks performed, often looking at
net yards per pass attempt. This metric is highly predictive; and while others may be more predictive, it is also far easier to calculate.

Perhaps the biggest change comes where it is labeled "Time of Possession/Rushing Yards". This metric was designed to determine who "controlled" the game. It has since been updated to look at how many rushing yards a team had per quarter.
As noted in a previous blog post, the more rushing yards a team scores later in the game, the likelier they are to win. The larger the number, the better that team "controlled" the game.

Overachiever/Underachiever refers to what the Cowboys' record should be, relative to their point differential for the whole season. In baseball, this idea is referred to as the
Pythagorean Expectation. In football, there is debate as to how to calculate such a record, but here, the exponent is 2.37: ((Points for^2.37) / (Points for^2.37 + Points Against^2.37)) * 16.

Finally, scoring efficiency has been tweaked. The idea here is to see how many points teams scored, relative to the number of yards they needed. The larger the bar and the bigger the number, the more efficient the team was. Simply put, it's points divided by yards, then multiplied by 15.457886 so that average is approximately 1. Using data from 2009-2016, we can also see if a team was overall good, average or bad in its efficiency. If the result is less than .949394, the team was inefficient. If the result is between .949395 and 1.057116, the team was average and gets a blue bar. If the result is greater than the aforementioned range, they were efficient and get a green bar.

Again, these metrics are meant to capture nearly everything that happened in a game that pertained to the result. Some of these metrics can also be used to forecast future games, but the intent is solely inference.

A Unique Cowboys Perspective

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The Dallas Cowboys are constantly watching film and studying the playbook for that added edge. Their fans also want to know anything that can help explain why their favorite team won or lost, and if there is a way to forecast how they will do and where they need to improve. Our newest data visualizations hope to do all of the above.

Before and during every Cowboys game, I will post on my various social media accounts some analytics that explain what is going on and predict what will happen. After the game, I will have one summary detailing what happened, using explanatory variables that are the best indicators for the outcome of any football game. Here is some extra information for each highlighted variable:

  • Turnovers are perhaps self-explanatory and the team with the better turnover ratio has a significant advantage.
  • Scoring efficiency goes beyond just the scoreboard. It's a ratio of (offensive yards/points). A team may have moved the ball but failed to score many points when near the end zone, so they were inefficient. Not only can each team's efficiency be compared, but each bar has a color: red for bad, blue for average and green for good. Respectively, these quality ranges are: 0-12, 12.01-18.5, 18.51-. These ranges came from the last ten years of NFL data, provided by Pro Football Reference.
  • The ratio (time of possession/rushing yards) looks at who was controlling the game effectively. Time of possession is not an effective indicator for success, but how well a team controls the ball while on offense is. The team with the better ratio earns the checkmark.
  • Overachiever/underachiever is a way to look at how well a team is doing for the season, relative to its point differential. In other words, if a team is has a strong record but all of their wins are close, they are overachieving. If they suffered a number of losses but they have been close, they are underachieving. This idea is calculated using a Pythagorean Expectation formula, something more commonly used in football: ((Points for^2.37)/(Points for^2.37 + Points against^2.37)). This winning percentage can then be multiplied by the number of games played to show where a team "should" be with its record.

Periodically there will be additional metrics to explain why the Cowboys won or lost, such as net passing yards/attempt, which takes into account sacks and incompletions as well as how many passing yards each quarterback is able to accrue. As more metrics become readily available, this summary will include them. To see these visualizations in real time, follow me:


Special thanks to
Fuzzy Red Panda for putting together these beautiful images and programs that advance sports analytics in such creative ways.

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