Despite the shared name, American football is a much easier sport to dissect numerically than association football. Significant measurable events which have a major impact on match outcome are much more numerous (a side will attempt to move the ball between 50 and 60 times per game) and the extreme stop start nature of the contest ensures that every inch of territory gained or lost comes with an attached change in game winning probability. How efficiently a team moves the ball either on the ground or through the air as measured by yards per attempt is an excellent proxy for team ability, enabling match predictions to go hand in hand with a couple of easily digestible headline statistics.
Almost as attractive from an analytical viewpoint is the inevitable strength of schedule issues which persist throughout a competition where 32 teams compete in a minimum of 16 games each. During this curtailed season, sides play other sides either once, twice or occasionally three times, but more often, not at all. Correcting raw efficiency stats for this constantly shifting strength of schedule can reveal just how good a side is, whereas traditional win loss records in a 16 game season can often fall short.
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In this guest post, I outlined the stats which are most useful in predicting match outcomes in the NFL and as the post season rapidly reaches a conclusion it seems an ideal opportunity to re visit the topic. Running the football comes with a much higher guarantee of retaining possession, but gains are often steady if unspectacular and passing the ball carries higher risks of incompletion or interception, but potentially has bigger rewards.
The average gain from running the football usually falls around four yards per carry and it is tempting to consider a side which averages in excess of four yards as above average. However, a side may have faced a large number of particularly porous run defenses and their rushing efficiency may be inflated by poor opponents, in reality they may actually be a below average running team.
This problem is most easily solved by comparing the average yards gained by a side to the average yards per attempt allowed by the combined pool of games played by their opponents to date. Tampa's 4.41 yards per carry over the year appears impressive, but it was achieved against defenses which allowed an average of 4.41 yards per carry, making the Buccs no more than an average rushing side in 2012.
Correcting efficiency stats on both the offensive and defensive side of the ball for opponent ability quickly produces a very potent indicator. The number of observations taken quickly reach three figures and after just four games a team's previous opponents will already have accumulated combined data from 16 matches, an entire season for a single team. Performance indicators taken in this way also begin to stabilizes very quickly. The best teams after a month of the season as measured by corrected efficiency rates are very often the teams involved in the post season lottery.
The uses of such rushing and passing stats are many and varied. At a team level strengths and weaknesses are readily seen and by pairing one team's set of stats with their opponents and regressing actual game outcomes against these numbers, pregame winning and losing estimates can be produced for future games.
In September of last year in the linked post, I ranked all thirty two teams based on their corrected rushing and passing abilities on both side of the ball after just four games had been played. Baltimore ranked number one and now contest the AFC championship game after beating Denver in overtime, San Francisco were second and they are also in the NFC championship game. Houston, ranked third after a month,also contest an AFC divisional game in New England and overall six of the eight teams who made it through to the divisional round were ranked in the top 10 as early as October.
Using Efficiency Stats To Predict Game Outcome.
| Team. | Offensive Run Efficiency | Offensive Pass Efficiency. | Defensive Run Efficiency. | Defensive Pass Efficiency. |
| Seattle @ | 1.13 | 1.18 | 1.04 | 0.88 |
| Atlanta. | 0.86 | 1.05 | 1.12 | 1.04 |
| Win % for Atlanta. | 45% | Points Spread. | +2 |
Seattle visit Atlanta later today. Seattle have been the biggest improvers since I last looked at these figures back in October. Their corrected passing stats have gone from being below average to well above average. The 'hawks are below average at defending the run, allowing 104% of average yardage, but very good against the pass, where they allow just 88% of average passing yardage. Proportionally it is better to be good at defending the pass or passing the ball compared to identical levels of competence against the run. The current NFL is an offensive game, but it is also increasingly an aerial one as well.
The bottom line, backed up by a couple of similar models is that Seattle is the better side here, even on the road. The caveats are that Atlanta along with Houston's opponents New England have regularly been able to out perform such models. Atlanta maintained an unlikely third down ability a few years back and New England consistently beat their stats (although Spygate may partly explain their better than expected results). Also Atlanta's bye week isn't incorporated into the final number.
Small sample sizes are no friend to models base on probable outcomes and you can't get smaller than one game, but Seattle have a shot at producing an all NFC West Championship game later today. Whatever the result, corrected efficiency stats are frequently potent tools in predicting who gets to the playoffs, whatever the outcome of the sudden death, post Christmas lottery.
Excellent article. Worth a mention perhaps is the approach of Bill Belichick to fourth and short. Statistically teams are better served by 'going for it' yet most coaches don't. They would rather play it less optimally but conventionally than draw attention to themselves and arguably put their jobs on the line!
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