This Super Bowl just feels old school. Not only because it will be played outside in the cold, but because this XLVIII’th edition features a matchup between two former AFC West rivals. The Denver Broncos and Seattle Seahawks were last in the same division in 2001, when Matt Hasselbeck and Shaun Alexander led the Seahawks and Brian Griese handed off to a stable of Denver running backs: Terrell Davis, Mike Anderson, and Olandis Gary. Now in different conferences, the Peyton Manning, Russell Wilson, and Marshawn Lynch versions are "a little" better, but no less evenly matched.
How evenly matched? Brian Burke from Advanced NFL Stats gives the Seahawks a slight advantage with a .52 win probability. In general, Burke’s game probabilities tend to agree with my simulations. The strength of our probabilities may differ, but most times the teams we identify as having the advantage are identical. This, however, is not one of those times, and it just so happens to be the Super Bowl.
After 10,000 runs, my simulation gives the Denver Broncos a slight advantage. According to my results, the Broncos have a .51 win probability, beating the Seahawks 53% of the time by an average of 1 point. Virtually, a statistical coin flip. But Burke attributes this kind of disagreement to the strength of schedule effect and to overweighing recent outcomes. However, my simulation does not account for strength of schedule and recency (or primacy). Instead it simply is a result of each respective team’s season-long offensive performance as well as the historical behavior of home and away teams.
In my opinion, this flip-flopping is more indicative of the ideal situation: the number one seeds from each conference battling for the Lombardi trophy. It really can’t get much better than this (well, obviously it could if the Eagles were playing). Regardless, it should be a good one, folks.
So which conditions give each team the best chance of winning? In 2013, both teams operated efficient offenses, Denver just a little more so. If during the Super Bowl the Broncos’ offense runs at its season’s efficiency rate of 76%, and the Seahawks’ offense runs at its 73% efficiency rate, then the Bronco’s win probability increases to .64. However, when the games are simulated under these conditions, the Seahawks win 52% of the time. Why the discrepancy? Turnovers, of course. The Broncos have turned the ball over seven more times than the Seahawks this season.
If turnover differential for the Super Bowl is zero, then the advantage is decidedly Denver’s, which wins 62% of the simulated games under this condition. And here is a great illustration of the relationship between offensive efficiency and turnovers: when the Broncos have one more takeaway than the Seahawks, then the Broncos win 82.6% of the time by an average of six points; if the Seahawks have one more turnover, they win 67% of the time by an average of three points. So in my opinion, the best battle on the field, the battle with the greatest potential for turnovers, and the one that will best determine the outcome, will be Seattle’s secondary against Denver’s receiving threats. Bold prediction: unless intangibles decide otherwise, this game will ultimately be won in overtime.
Going back to the old school thing, I’ll end with one of my favorite quotes about a player who also hailed from the AFC West. From the Voice of God, Philadelphian John Facenda… "He was lightning, in a game ruled by thunder. And he lit up the Super Bowl sky with a history making performance." Who will this apply to Sunday?
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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.
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.