Category: Statistical Analysis

Can Brock Osweiler Possibly be the Right Choice for the Browns?

On By Kevin ColeIn Statistical Analysis1 Comment

This morning Browns Head Coach Hue Jackson named Brock Osweiler the starting quarterback for the team’s first preseason game against the New Orleans Saints on Thursday. The news was at least somewhat surprising, as the trade by which the Browns acquired Osweiler was largely a salary dump by his former team, where the Browns acquired a second round pick as compensation for taking Osweiler off of the Houston Texans’ hands.

While Osweiler is being paid starter money, the consensus opinion going into training camp was that the Browns would start the season with one of the two quarterbacks getting reps with the first-team offense, second-year signal-caller Cody Kessler or second-round rookie DeShone Kizer.

Osweiler was bad for the Texans last year, really bad. Osweiler’s 5.8 yards per attempt ranked dead last among 30 NFL passers with at least 300 attempts last season, with a material gap between Osweiler and 29th ranked Carson Wentz (6.2 yards per attempt). Continue reading “Can Brock Osweiler Possibly be the Right Choice for the Browns?” →

Does Spreading the Ball Around Make a Passing Attack More Efficient?

On By Kevin ColeIn Statistical AnalysisLeave a comment

Part of analyzing an NFL team’s passing game is measuring the distribution of passing stats among its receivers: targets, yards, touchdowns, etc. A good way to do this is looking at the percentage distribution to different receivers, i.e. the WR1 received 25 percent of receiving yards, the WR2 got 15 percent, and so on. This is useful when projecting stat lines for receivers, but doesn’t give us an easy way of comparing generally how concentrated one passing game is versus another.

A commenter at Chase Stuart’s Football Perspective gave him an idea from the finance world for calculating portfolio concentration, which Stuart used to conclude that teams are spreading it around more these days. The formula sums the squared passing yard ratios for each receiver to form a single number, or what Stuart calls the concentration index. The more spread out a passing game, the lower the concentration index, and vice versa.

I decided to dig into concentration index a bit more to see if we can learn anything about the benefits or costs of have a diversified passing game. Continue reading “Does Spreading the Ball Around Make a Passing Attack More Efficient?” →

Should the Rams Increase Todd Gurley’s Role in the Passing Game?

On By Kevin ColeIn Statistical AnalysisLeave a comment

Depending on whom you listen to, you should expect the Rams to either increase or lessen the extent to which they use Todd Gurley in the passing game. At Rams minicamp, NFL.com’s Gregg Rosenthal witnessed Gurley seeing a lot of targets and often appearing to be the primary read. While others see the addition of receiving back Lance Dunbar and new head coach Todd McVay’s heavy passing-game usage of Chris Thompson in Washington logically leading to Gurley seeing fewer passes.

Gurley’s role in the passing game this season will also be highly dependent on his ability, in addition to the Rams’ personnel and passing scheme. Gurley saw decent, but not spectacular passing usage in college, and has carried that forward to the NFL. Continue reading “Should the Rams Increase Todd Gurley’s Role in the Passing Game?” →

Should a Receiver-Needy Team Acquire Jeremy Maclin or Eric Decker?

On By Kevin ColeIn Statistical Analysis3 Comments

We’re into the summer doldrums of the NFL calendar, but got a small dose of excitement over the last week. Two top wide receivers are in limbo: Jeremy Maclin officially hit the free agent market last week, and has been making the rounds to potential suitors; and Eric Decker – initially expected to be released by the Jets – is now reportedly the subject of trade talks.

It’s fair to assume that the two receivers will cost roughly the same in terms of a new contract, as both are at, or approaching, 30 years old and have been productive – when healthy – throughout their careers. Decker isn’t a free agent, so his cost is currently higher in draft capital, although I’d put a low likelihood on the Jets gaining more than a late-round pick for his services.

The receivers share fairly similar box-score stats, both averaging around 70 yards per game over the last four years. But Decker has been a more dominate touchdown scorer. While box score stats are good at measuring the effect a receiver has when he is targeted and catches the ball, it doesn’t fully capture his influence on the entire offense. Continue reading “Should a Receiver-Needy Team Acquire Jeremy Maclin or Eric Decker?” →

Deshaun Watson, Mitchell Trubisky and the Importance of Sample Size

On By Kevin ColeIn Prospect Analysis, Statistical Analysis4 Comments

I tend to ignore the specifics of most NFL mock drafts, but I was happy to see Rotoworld’s Josh Norris recognize the importance of sample size while recently predicting that the analytical wizards with the Cleveland Browns will prefer Deshaun Watson to Mitchell Trubisky come draft day.

(We should mention that everything written below regarding Watson’s larger sample also applies to Patrick Mahomes, who had more pass attempts than Watson at a similar yards per attempt.)

To those who are familiar with statistics generally, the concept that the Browns would want a larger sample for their potential franchise quarterback isn’t tremendously difficult to grasp. But it was still a pleasant surprise to see someone in the larger draft community weave this thinking into his analysis, especially when some mock drafts still forecast the Browns to make terrible strategic decisions from an analytical perspective, including taking a running back in the middle of the first round. Continue reading “Deshaun Watson, Mitchell Trubisky and the Importance of Sample Size” →