I examined drive success based on how 'momentous' the manner in which the offense gained possession. Admittedly, that analysis only measures one aspect of momentum. In this post, I'll take the analysis a step further and look at how a team's chances of winning are affected following several momentum-swinging types of events. This approach examines the potential effect of momentum on the entire remaining part of a game, not just on the subsequent drive.
Like the previous analysis, I relied on how possession was obtained as an indication of a momentum-swing. For all drives from 1999-2013 ( through week 8), I compared a team's expected chances of winning (based on time, score, field position, down and distance) with how often that team actually won. I divided the data among three categories: possession obtained following a momentous play, possession obtained following a turnover on downs, and possession obtained following a non-momentous play.
Momentous obtainment includes fumble recoveries, interceptions, muffed punts, blocked kicks, and blocked field goals. I excluded missed field goals from the analysis because it was unclear to me how momentous they are. They are often thought of as big momentum changing events in close games but are too common (almost 20% of all kicks) to truly be momentous.
The reason this is such a useful method for testing momentum is because the Win Probability model is a function that estimates a team's chance of winning a game based on a snapshot of the game state, and it is agnostic to all previous events. In other words, the WP model is, by design, completely unaware of how a team got to where they are in a game.
For example, the WP model cannot tell the difference between a game in which Team A started hot with a 14 point lead only to give up 21 unanswered points, and another game in which Team B started the 2nd half behind by 17 points but has closed the lead to only 7. In both cases the team with possession is down by 7, but one team has all the apparent momentum and the other team doesn't.
Because the WP model is ignorant of past events and only the present state, true momentum-swinging events should cause the model to break--if momentum is real, that is. In other words, teams with positive momentum should win more often than the WP model predicts, and teams without momentum should win less often than the WP model predicts. According to the example above, both teams are in identical game states (down by 7 with the ball), and the WP model would expect them to win with equal probability. But Team A, which started hot but lost the momentum, would be expected to win less often than Team B, which started cold but has the momentum.
So let's test the accuracy of the WP model following various methods of obtaining the ball. The chart below compares the actual winning percentage of teams according to how the model estimated their probability of winning at the point of obtaining possession. (Ideally, a perfectly calibrated WP model would produce a perfectly diagonal line--When it says teams have a 0.65 probability of winning, they win exactly 65% of the time.) Deviation from the ideal diagonal will indicate the effect of momentum-swinging events on actual game outcomes.
The plot is broken out by how possession was obtained. The blue line represents momentous obtainment. The red line represents obtaining the ball following a turnover on downs, and the green line represents non-momentous obtainment. The idea of momentum suggests the blue and red lines would be above the green line throughout the range of expected WP.
In the chart above, we can see that teams that obtain possession on downs win less often than we would expect given the game state. This is contrary to the expectation based on the momentum hypothesis. There does not appear to be any significant difference between the effects of other momentous and non-momentous events.
The previous article examining momentum excluded all drives that began in the 4th quarter. The purpose of that exclusion was to eliminate situations in which a team with a lead did not need to try to score to seal the win. But in this analysis, the WP accounts for such dynamics and the exclusion is not warranted. However, in the interest of consistency here is the same analysis with 4th quarter drives excluded.
It appears teams that obtain the ball following turnovers, blocks, and muffs do not win any more frequently than teams that obtain the ball by non-momentum-swinging means. And of particular interest, teams that obtain the ball following a failed 4th down conversion attempt do not win any more frequently than those who receive the ball through typical punts and kickoffs.
These results are consistent with the results from the previous drive-level analysis. Momentum-swinging events including turnovers, blocks, and muffs do not appear to inspire a greater level of performance. Indeed, the opposite may be true.
The next installment of my momentum study will get down to brass tacks and look at how the concept of momentum affects in-game decisions. And beyond that, I'll dig deeper into the general notion of momentum in football, applying a novel statistical approach to measure the 'streakiness' of games. The results may be surprising.