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.
Last week’s simulation successfully projected a Philadelphia Eagles’ victory over the New York Giants. They won with an offense that operated at 70% efficiency and a defense that created four turnovers (thanks Eli!). It helped, again thanks to Eli Manning, that the Giants offense only operated at 48% efficiency. Under these circumstances, the simulation projected a 31-13 Eagles win, not too far from the actual score, 36-21.
Today, the Eagles travel to Tampa Bay to play the Buccaneers. This week’s simulation is based on each team’s offensive efficiency through the season (Buccaneers = 65%, Eagles = 68.9%). And after 10,000 runs, the Eagles win 57% of the time by an average score of two points (actually, in their 5,747 simulated wins, they win by an average score of 26-15; in their 4,253 simulated losses, they also lose by an average score of 26-15). The shape of the scatter plot is interesting, similar to the game against the Giants. The trend seems to be for a lower scoring game, or more accurately, each team tends not to score a lot of points at the same time in a single game (so don’t look for scores like 34-31).
According to this model, the Eagles win the majority of the time thanks largely in part to their better offense, however, the Eagles have less room for error than they did against the Giants. When the turnover differential is zero, the Eagles win 75% of the time by an average score of 22-17, but when the turnover differential moves to -1, they lose a slight majority of the time.
Ignoring the simulation for a moment, Tampa Bay is coming off a bye, so with two weeks to prepare, playing at home, they can be a potentially dangerous opponent. One that shouldn’t be overlooked.
*Simulation Details (because someone will ask)
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.