Computing Technology Assignment 2015 Nfl

A startup venture, EndGame Technologies, has designed novel computer modeling software to assist National Football League coaches with critical play-calling decisions–the kind that often determine the outcome of the game. Should a team punt on fourth down–or go for it? Or attempt a two-point conversion after a touchdown? The software, called ZEUS, is designed to answer such questions by calculating the consequences of each decision in a matter of seconds.

Head ZEUS researchers Chuck Bower and Frank Frigo want to revolutionize football by changing the way plays are called. Already, their statistics on past NFL games have revealed that teams consistently lose approximately one game per season by making the wrong play calls in critical situations. And with only 16 games in an NFL season, each win or loss is of paramount importance.

Football is a game of strategy and risk management. Each coach goes into a game with a plan, explains Dwight Smith, head football coach at MIT. Although every variable is considered beforehand, adjustments have to be made as the game progresses. ZEUS is meant to help coaches make those on-the-fly decisions. The software was created by mining historical NFL data and developing distribution curves of success rates for individual actions, such as how far a running back carries the ball.

Coaches could use ZEUS at any point in the game, by entering a set of variables, such as the score, field position, possession, down, and time remaining on the clock. Then the user enters two play-call options and the software analyzes each one separately, playing the game to its conclusion 100,000 times. During each iteration, ZEUS considers a different scenario.

All the decisions about play calls that occur after the initial play are based on historical NFL data. This data isn’t specific to a particular coach, though; it’s based on records from a wide variety of coaches. (The researchers call this algorithm the “generic” NFL coach.) In a matter of seconds, ZEUS displays the Game Winning Chance–the play that gives the team the highest probability of winning.

Unlike interactive football simulations, which require about the same amount of time to play out as a real game, ZEUS performs its calculations and recommendations in real time, displaying the computer’s results before the next snap.

According to Bower, the challenge for them now lies not in the technology, but in getting NFL teams to adopt it. “We have shown it to 10 NFL teams, [everyone] from managers to head coaches,” he says. “But at this point no one has stepped forward and said we are ready to pay for the product.”

The problem: ZEUS is still illegal under NFL guidelines. The league doesn’t permit computers on the sidelines or in the coach’s booth on game day. Although there are no restrictions on using high-tech analytical tools off the field, the traditional elements of strategy continue to be preserved on game day.

“We are very resistant to changing for the sake of changing,” says Brain McCarthy, NFL spokesperson. “Part of the appeal of the NFL is that it is man against man against elements and unscripted drama. When you add technology that could directly influence play on the field it has the potential of detracting from the overall product and enjoyability of the game.”

Bower and Frigo aren’t the only ones pushing for a technological revolution in the NFL. KC Joyner, called “The Football Scientist,” and a regular contributor to ESPN Insider, uses game film to track, tabulate, and analyze nearly every measurable statistic in an NFL game. These statistics, complied and explained in Scientific Football 2006 (pdf), use a performance-based metric system with the goal of “quantifying everything and putting it into perspective.”

Joyner believes that if an NFL team doesn’t take advantage of the latest technology, it will be hurt in the long term. “Nontechnological teams can still get good players, and some things will work, but as they get further behind the curve, it is going to catch up to them at some point,” he says.

Whether or not the league accepts ZEUS and teams decide to use it remains to be seen. For now, it can be implemented only in practice situations.

Bower and Frigo recently added customization–the ability to enter the characteristics of specific teams–and also developed a second application, ZEUS PPV (“Player Position Value”), which determines the value in incremental wins/losses per season of individual position players.

“Once [NFL] teams begin to embrace technology, the entire league will advance and be more successful,” says Joyner, “It is going to take one successful coach looking for an edge, willing to take a chance.”

“There’s a lot of discussion about it, both inside a department as well as across the field,” said Randy H. Katz, a professor in the electrical engineering and computer science department at the University of California, Berkeley, who discovered in one year that about 100 of his roughly 700 students in one class had violated the course policy on collaborating or copying code.

Computer science professors are now delivering stern warnings at the start of each course, and, like colleagues in other subjects, deploy software to flag plagiarism. They have unearthed numerous examples of suspected cheating.

At Brown University, more than half the 49 allegations of academic code violations last year involved cheating in computer science.

At Stanford, the alma mater of the founders of Google, Snapchat and countless other internet wonders, as many as 20 percent of the students in one 2015 computer science course were flagged for possible cheating.

And at Harvard, where Computer Science 50 is practically its own brand, with T-shirts, slickly produced videos and an online audience of thousands, the class distinguished itself last fall in a more dubious way: According to The Harvard Crimson, more than 60 students were referred to the university’s honor council, a committee that reviews allegations of academic dishonesty, such as plagiarism, and violations of the honor code.

In interviews, professors and students said the causes were not hard to pin down.

To some students drawn to the classes, coding does not come easily. The coursework can be time-consuming. Troves of code online, on sites like GitHub, may have answers to the very assignment the student is wrestling with, posted by someone who previously took the course.

“You’ve got kids who were struggling with spending a third of their time on their problem sets with the option to copy from the internet,” said Jackson Wagner, who took the Harvard course in 2015 and was not accused of copying. “That’s the reason why people cheat.”

Complicating matters is the collaborative ethos among programmers, which encourages code-sharing in ways that might not be acceptable in a class. Professors also frequently allow students to discuss problems among themselves, but not to share actual code, a policy that some students say creates confusion about what constitutes cheating.

The executive committee at Yale, where five students were accused of copying code last fall, though a couple had charges withdrawn, acknowledged as much.

“It is often such a complex task to read these guidelines,” wrote the committee chairman, Paul North, a professor of German, “that the code to be written seems simple in comparison.”

Penalties can be as small as a zero on an assignment, which is what the Purdue students received; they were also docked a letter in the course’s final grade. (Dr. Dunsmore credits awareness of such penalties with a reduction in cheating in his courses over the last decade.) More serious or repeated infractions can lead to failing the course, or even suspension.

At some colleges, computer science courses have become a leading source of academic dishonesty complaints. But many computer science professors reject any notion that their students are more prone to cheating than others. Cheating scandals have cropped up in other subjects in recent years, including in a government class at Harvard and in a Dartmouth class on sports, religion and ethics.

“It’s mostly that a lot of computer science professors actually check,” said Alex Aiken, who heads the computer science department at Stanford.

Though coding is a foreign language to most people, the principles of plagiarism are the same as with papers written in English.

Sometimes it is acceptable for lines of code to be identical, if the code is performing a routine task or one that cannot be done a different way. But other times it is a red flag.

Plagiarizers might try to cover their tracks by replacing words with synonyms, like 0 instead of NULL, or replacing the original author’s names for variables with their own. In a hypothetical example of cheating he sometimes sees, Thomas Doeppner, the vice chairman of computer science at Brown, provided a snippet of coding for a program that finds a path through a maze:

struct visit_list node;

node.row = row;

node.col = col; = NULL;

Someone copying the code, he said, might render it this way:

struct rooms_visited room;

room.r = r;

room.c = c; = 0;

Usually, anti-cheating software can uncover these tricks. One, developed by Dr. Aiken, is called MOSS, for Measure of Software Similarity. Another program, developed by a British company, Codio, monitors students’ keystrokes; a sudden burst raises questions about where the code came from.

At Harvard, David J. Malan, the CS50 professor, introduced a “regret clause,” letting students who cheat and admit it within 72 hours receive an unsatisfactory or failing grade on the assignment, and avoid further discipline — unless they do it again.

But in last fall’s CS50, an unusually large number of cheating allegations — involving more than 60 out of 655 students, The Crimson reported — were reported by Professor Malan or his staff to Harvard’s academic honor council.

Professor Malan said one reason for the large number of cases was that instructors were scrutinizing students’ work more carefully than before. He also said that since students could use the regret clause, instructors felt more comfortable going to the honor council when students had passed up that chance.

He referred questions about the outcomes of the cheating complaints to a university spokeswoman, who said she could not discuss the cases because of student privacy.

Two Harvard students who said they were familiar with some of the violations, and who wanted to be anonymous for fear of repercussions from the university, described one of the more blatant examples: A student submitted code using a computer language, PHP, that had been taught in a previous year, but not last fall.

Some students suggested that in other cases there was a fine line between reasonable sharing and improper copying, particularly because the professor encourages students to discuss problems with one another.

“There is a gray area as to how much help one can get,” said Javier Cuan-Martinez, the president of the Harvard Computer Society, who took the course two years ago.

Professor Malan, in an email, said the guidelines in the course syllabus were clear. One rule says that when asking for help, you may have your code viewed by others, but you may not view theirs. “Whenever a line is actually crossed,” Professor Malan wrote, “it’s often that one.”

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