Today’s Expected Points models have evolved to account for significantly more game context. Simply put, Expected Points describes how many points, on average, a team is expected to score on a possession given a particular field position. Adding together all the values for all potential outcomes yields the total Expected Points. When a team possesses the ball at the 50 yard line, the probability that their drive ends with a touchdown is 30%, and because the value of a touchdown is 7 points, the expected points from touchdowns on the drive is therefore 2.1 (0.3 * 7). As teams approach their opponent’s endzone, the probability of scoring (Touchdowns and Field Goals) increases. Carter and Machol did this by adding together the point value and probability of all potential outcomes of a possession. Their paper quantified a concept intuitively understood by all football fans - possessing the ball closer to your opponent’s endzone is better than possessing it further away. The concept of Expected Points (EP) was first introduced in a 1970 research paper by Virgil Carter, who was the Bengals starting QB at the time, and Robert Machol, who was a professor at Northwestern. But what is NFL EPA? This post will help explain the intuition behind Expect Points, how Expected Points Added is calculated from Expected Points, and how to think about both in the context of analytics. If you follow the NFL, and especially if you follow football analytics, Expected Points Added (EPA) is a metric you’ve seen being used more and more.
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