We all know how vital offensive lines are in the NFL. For a perennial winning franchise like the Eagles or 49ers, the O-line can be a consistent anchor for a team’s success that can steady an offense and open up opportunities for dynamic playmaking and a balanced offensive attack that can ripple throughout a team, not just on offense but in all three phases. It’s not unusual that the teams with the best offensive lines in the league are also the teams that we see make the deepest runs in the playoffs.
On the flip side of this, perennial losing teams like the Giants and even the Texans (although their weak division and occasional exceptional QB play will get them more winning seasons than the should) the O-line can be an absolute nightmare that can haunt the organizations and the fans and cause decades of disappointment in which generationally great offensive players like Andre Johnson, Adrian Foster, Odell Beckham Jr., and Saquon Barkley all fail to take their teams to the promise lands.
The incredible impact O-lines have on teams in the real NFL has never seemed apparent in Madden 26, at least in my opinion. I’ve never felt like the O-line has ever been a significant component in Madden. With all the intricacies about quarterbacks, running backs, receivers, and even all the focus on other game aspects like trade logic and the accuracy of Franchise mode simulations, the O-line ratings and their in-game impact have always taken a back seat, but not today!
Today I’ve put on my white laboratory coat and my protective goggles and tried my hand at some deep scientific research into Madden and how its O-Line ratings impact the actual game.
The Setup
In this experiment, I created four O-line rosters for four teams with varying degrees of talent. First and foremost, the 49ers have the ultimate all-pro O-line, consisting of the top players in the game at each position on the line. Next, I went in the opposite direction with the Texans and gave them the worst starting player in the game at each O-line position. These two teams represented the upper and lower boundaries for what should be considered “realistic” in the game. They represent the best and worst possible options if you were playing a typical franchise mode and building a roster.
I also wanted to go above and beyond the “realistic option and see what would happen if we max out all the main blocking stats (pass blocking, pass blocking finesse, pass blocking power, run blocking, run blocking finesse, and run blocking power) on all five starting O-linemen, which is what I did for the Eagles. I then did the opposite to the New York Giants, for whom I gave a 25 attribute rating to each O-lineman’s blocking attributes.
Here’s what the overall looked like for each of my experiment team’s starting O-line:
49ers O-line:
Trent Williams 97 OVR
Quenton Nelson 95 OVR
Creed Humphrey 96 OVR
Quinn Meinerz 97 OVR
Lane Johnson 99 OVR
Texans O-line:
Aireonte Ersery 72 OVR
Jonah Savaiinaea 72 OVR
Nick Gates 71 OVR
Juice Scruggs 72 OVR
Trey Pipkins 70 OVR
Giants O-line:
Andrew Thomas 25 OVR
Jon Runyan 30 OVR
John Michael Scmitz jr. 26 OVR
Greg Van Roten 30 OVR
Jermaine Elumunor 23 OVR
Eagles O-line:
Jordan Mailata 97 OVR
Landon Dickerson 99 OVR
Cam Jurgens 97 OVR
Tyler Steen 95 OVR
Colton McKivitz 96 OVR
Important note:
To establish a constant in this experiment and limit outside factors, all other starting offensive players on these four teams were edited to be roughly similar in overall, at roughly 83-84. This helped ensure we were examining only the impact of offensive line play, without dominant performances from players like Saquon Barkley, Nico Collins, or Christian McCaffrey skewing the data.
Live Play Experiment
In order to test how these teams play on live snaps, I ran each of them through a standard training camp session in which each team played five pass snaps and five run snaps, each using the same play against the defense (the Browns) using the same defensive play. I recorded how long it took the Browns to sack the QB (I intentionally didn’t pass the ball, nor did I move from the initial position the QB gets the snap from), and how many yards each run play went for. Here are the results.
Offensive play: stick tight Y off/ power run
Defensive play: cover one hole 4-3 over Browns
Average time to sack:
Eagles: 3.782 seconds
49ers: 3.774 seconds
Texans: 3.14 seconds
Giants: 2.732 seconds
Yards gained on average
Eagles: 3.6 yards per run
49ers: 3 yards per run
Giants: 2 yards per run
Texans: 1.4 yards per run
Season Sim Experiment
To truly understand whether a dominant O-line makes a team a winner in Madden or if a terrible O-line makes a loser, I had to put these four teams through a season-long simulation. Here’s what happened.
Rushing yards
Eagles rushing yards: 2012 (3rd)
49ers rushing yards: 1916 (6th)
Texans rushing yards: 1510 (20th)
Giants rushing yards: 1018 (31st)
Passing yards
49ers sacks allowed: 20 (1st)
Eagles sacks allowed: 32 (5th)
Texans sacks allowed: 53 (28th)
Giants sacks allowed: 82 (32nd)
Total yards
Eagles total yards: 5964 (1st)
49ers total yards: 5603 (9th)
Texans total yards: 5403 (16th)
Giants total yards: 3536 (32nd)
Record
Eagles: 12-5
49ers: 10-7
Texans: 4-13
Giants: 0-17
Final Results
The best takeaway from this experiment is that there is a correlation between O-line attributes and game results, but the correlation isn’t always linear. Yes, an artificially terrible O-line full of 20-30 overall players is going to give you terrible in-game performances, and a team with 90-99 overall O-linemen are going to give you great performances, but that middle section where the majority of all teams exist in is a gray area that is going to depend on your skill position guys than your to determine where your team lies. At the end of the day, two extreme ends of the spectrum can cause your O-line to affect your team’s overall performance, but outside those two extreme ends, your O-line talent still doesn’t seem to make much of a difference in your team’s success.
