In a huge division game with significant playoff implications, the Dolphins trailed the Patriots 20-17 with a little over three minutes remaining and three timeouts in their back pocket. Facing a 4th-and-5 from their own 45-yard line, Jim Nantz asked Phil Simms whether he would punt in that situation, to which Simms replied "Absolutely, punt it!" The logic here is that with three timeouts remaining, the Dolphins could stop the Patriots and force a three-and-out, getting the ball back with another chance to drive down and tie or win the game.
Let's look at the baseline win probability numbers:
Game probabilities for week 15 are available at the New York Times. This week I note the high playoff leverage of the NE-MIA game.
All of the numbers below come from Chris Cox at NFL-forecast.com. His app uses the win probabilities from the ANS team efficiency model to run a Monte Carlo simulation of the remaining NFL games thousands of times. Based on current records, our estimates of team strength, and knowledge of the NFL's tie breaking procedures we can come up with some pretty interesting predictions of how each team will fare come the end of the season. If you want to use a different model or just fiddle with the numbers by hand, go ahead and download the app yourself.
Week 14's biggest movers
Dallas took a major hit by losing to Chicago. Coupled with Philadelphia's win against Detroit, this caused the Cowboys' playoff odds to drop 18%.
San Francisco's big win over the rival Seahawks propelled them to their current 93% playoff probability. The jump came mostly at the expense of the Panthers, who lost their big rivalry game against the Saints. The Saints are now one game up on the Panthers and considered a much better team by the ANS model, so a New Orleans divisional championship is now deemed 95% likely.
Brian Burke is back on the show to break down his latest work. He explains the details of the updated win probability formula and the story behind the brand new New York Times 4th Down Bot. Dave and Brian also discuss his latest post analyzing "streakiness" and how it can measure momentum.
They then dive into the wildly entertaining week 14 slate of games, beginning with a look at Keith Goldner's intentional touchdown post and a review of the best (Matt Prater) and worst (Philadelphia/Detroit) of the week 14 kicking game. Brian lays out the interesting math behind the Alabama/Auburn missed field goal return, and how the analysis behind it presents some tricky challenges. The show ends with a look at late season winter weather, and how it may affect teams as they prepare to battle for the chance to play outdoors in this year's New York Superbowl.
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This is the 4th part in my series on examining the concept of momentum in NFL games.The first part looked at whether teams that gained possession of the ball by momentum-swinging means went on to score more frequently than teams that gained possession by regular means. The second part of this series looked at whether teams that gained possession following momentous plays went on to win more often than we would otherwise expect. The third part focused on drive success following a turnover on downs, which is often cited by coaches and analysts as a reason not to go by the numbers when making strategic decisions.
This article will examine how 'streaky' NFL games tend to be. If momentum is real and it affects game outcomes, it would result in streaks of success and failure that are longer than we would expect by chance. But if consecutive plays are independent of previous success, the streaks of success and failure will tend to be no longer than expected by chance. This method of analysis does not rely on any particular definition of a precipitating momentum-swing, as it looks at entire games to measure whether success begets further success and whether failure leads to more failure.
For momentum to have a tangible effect on games, it does not require completely unbroken strings of successful or unsuccessful plays. But if success does enhance the chance of subsequent success, then the streaks of outcomes will be longer than if by chance alone.
For this analysis, I applied the Runs Test to the sequence of plays in a game. This produces a statistic indicating how streaky a string of results is compared to what would be expected by chance. For example, consider the following 3 strings of results of flipping a coin 8 times:
HTHTHTHT, HHHHTTTT, HTTTHTHH
The Runs Test works like this: