Simulating the Game: Eagles vs. Bears

Andrew Weber-USA TODAY Sports

Bleeding Green Nation simulated the game between the Philadelphia Eagles and Chicago Cubs Bears. Give the Eagles a slight edge.

In his New York Times post yesterday, Brian Burke from Advanced NFL Stats gave the Philadelphia Eagles a 64% chance of defeating the Chicago Bears Sunday night, citing results from a Monte Carlo simulation (by the way, he also gave the Washington Redskins a 54% chance of beating the Dallas Cowboys).  While I appreciate the confidence and think the Eagles have a good chance of winning, that’s a little too optimistic for me.

On the season, the Bears offense has been very good, efficiently operating at a 74% rate, which is a little better than the Eagles 72% rate of offensive efficiency. The Bears run the ball well (Matt Forte: 1,200 yards, 4.7 Y/A) and have a scary passing attack (Alshon Jeffery + Brandon Marshall: 2,450 yards, 17 touchdowns).  They do a decent job of protecting the ball (19 turnovers) and, unlike the Lions, tend not to shoot themselves in the foot with penalties (39 offensive penalties on the season, compared to the Eagles’ 49).  Given the historical performance of home and away teams under similar conditions, this projects to be a tighter game than Burke’s win probabilities suggest.

After 10,000 runs of my own simulation, the Eagles beat the Bears just 52% of the time and have a 50.6% win probability.  If Nick Foles can operate the Eagles’ offense at or above their season’s average of offensive efficiency, and the defense can hold the Bears at or below theirs, the Eagles win 63% of the time and have a 61% win probability.

Of course, as always, much will depend on turnovers.  If the Eagles give up one more turnover than they take away, their win probability drops to 37%.  Conversely, if the Eagles secure one more turnover than they give away, their win probability increases to 63% (plus 2 = 75%, plus 3 = 84%).  Ultimately, I’d give the Eagles a slight statistical advantage, as well as an edge with intangibles, as they try to redeem themselves from the loss to Minnesota.  And for what it’s worth, this model projects the Redskins/Cowboys game as a statistical coin flip.  After 10,000 runs, the Redskins win 5,008 times; the Cowboys 4,992.

Interact with the viz below to see more on the Eagles/Bears.

Note: NFL simulations are far from an exact science. They attempt to mathematically project the future based on history and past performance, but they can’t account for everything. A stiff breeze, a tipped ball, a freak injury, a rolling fog bank, an ol’ coach’s return, or simply a change in player attitude can alter results in a large way. Instead, simulations give us a blurry view of a series of possibilities among an infinite number of potential realities. But they’re fun. If you believe in parallel or multiple universes, then one of these simulated results could possibly occur.

*Simulation Details

The simulation is based on my home field advantage (HFA) research, which shows how there have been small but distinct and different offensive efficiency behaviors between home teams and away teams in the NFL. And not surprisingly, turnovers play a large role in equalizing the playing field. Offensive performances throughout the season were entered into a logistic regression formula born from the HFA research, and randomized according to standard error values and turnover differential.

Step 1: Calculate Offensive Efficiency (OE). I used Chip Kelly’s definition for this:

(Rushes + Completions) / (Total Off Plays + Offensive Penalties)

If you check out the HFA research, there’s a really strong correlation between offensive efficiency and team success.

Step 2: Calculate Win Probabilities using the logistic regression formula that correlated OE to team success. Here’s the formula:

Win Probability = 1 / (1 + e^-((A*OE+error value) + (B*Turnover Diff + error value) + C)), where A, B, and C are constants.

Step 3: Convert the results from Step 2 into points using a linear formula:

Points = A*(Win Probability for Eagles) + B*(Win probability for Opponent) + C, where A, B, and C are constants.

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