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Modeling the Probability of Winning an NFL Game

Modeling the Probability of Winning an NFL Game


So what is it that makes this model tick? Three major factors working in tandem:

1. Vegas Odds

Sports bookies begin each week by determining the point spread, and they decided the Colts were2.5-point favorites. At the beginning of a game, because there is plenty of football left to be played, the odds of winning stay relatively close to what the bookies say. The Chiefs leading by 7 in the first quarter didn’t give us (or the model) enough information to pick a winner.

2. The Scoreboard

As the game progresses, though, the scoreboard becomes key. The model can tell us how likely teams in the past have been to win based on the current score and time left in the game. ESPN’s model is based on 10 years-worth of NFL play-by-play game data, which is a lot of plays. At halftime, with the Chiefs up 31-10, it calculated that they had a 96.4% chance to win. A team winning by 3 touchdowns at half is historically an almost sure pick.

3. Field Position

Towards the end of the game, as there is not much time left, a team’s chances to take the lead are more dependent on where they are on the field and who has the ball. For example, a team that is up by 3 has a much better chance of winning if they’re on the opponent’s 1-yard line than on their own 1-yard line. Why? Because, historically, being on the opponent’s 1-yard line gives a team an expected value of 6.9 points on the next play.

Stashed in: Football, Big Data!, Big Data, Football, So you're saying there's a chance..., Freakonomics, Sports Analytics

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I find this kind of analysis fascinating.

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