UWM Professor Paul Roebber led a team of researchers who developed a mathematical model that can predict the outcome of a single NFL game based on the teams' momentum.
Newswise — Psychologists have long dismissed sports “momentum” – the notion that successful performance by one team breeds continued success for that team – as a false pattern perception.
But researchers at the University of Wisconsin-Milwaukee used machine learning to show that momentum in a football game is a real phenomenon. And, as fans will tell you, it can improve a team’s chances of winning, regardless of the pre-game favorite status and even the advantage gained by running out the clock.
“Our model’s performance is comparable to the point spread at the start of the game and improves thereafter with little bias,” said Paul Roebber, a UWM professor of mathematical sciences who led the research team.
Using play-by-play data for 10 years of NFL regular season games, the scientists trained a neural network – where the algorthms perform like neurons in the human brain – to predict a team’s probability of winning.
The model defines momentum as an increase in a team’s “win probability” over the course of at least three successive changes in ball possession based on factors such as what time it is in the game, what down it is, what the score is, and the team’s location on the field.
“So, with this definition of momentum, it’s typically a pretty extended period of time,” Roebber said.
The project was published in June in the journal PLOS ONE.
Roebber explained that both the offense and the defense contribute to win probability, in their model.
To illustrate how momentum bumps up win probability incrementally, Roebber gave the following scenario:
“The home team might improve its chances of winning through an offensive possession, and then the visitors get the ball,” he said. “And perhaps the home team defense holds their opponents to a ‘3-and-out,’ which gives the home team possession of the ball in good field position. That would be considered two positive changes in possession, where the home team has improved their chances of winning on both sides of the ball.”
The researchers also found that negative momentum exists. Because their model is defined relative to the home team, negative momentum means the visiting team is effective on both offense and defense. So positive momentum by one party coexists with negative momentum by the opposing team.
Previous research trying to establish a correlation between momentum and game outcomes have looked primarily at the streaks in performance from an individual player in a game. In those instances, no associations could be found, Roebber said, because the model wasn’t describing team performance.
“When you’re talking about a team sport like football, my view is that momentum is a function of all the players,” he said. “And so, you really need to look at the collective performance of the team.”
The researchers’ model also had some success in predicting when momentum was most likely to emerge during a game, by applying a second neural network.
Two interesting aspects of the work had to do with controlling for clock management and a game’s favored team.
Toward the end of a game, the leading team wants to run out the clock because plays that burn time without giving the opponent the opportunity to advance increases their chances of winning. Likewise, the researchers also took into account the pre-game odds, after initially not including them in the data. Once incorporated, those data didn’t change the model’s results.
Roebber added that the model is designed to look at momentum in a single game, but not for multiple games across a season, a situation that would require a different machine-learning model.
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