NFL games are tighter and more exciting this year
The 2022 NFL season has provided some of the most thrilling games of the past two decades. We dive into the numbers to show how exciting this season has been so far.
Last week Josh Hermsmeyer at FiveThirtyEight.com wrote about how NFL games this season have never been closer. That has led to a season of tight games and late comebacks, making it one of the more fun regular seasons in recent memory.
His argument was teams are using a mixed bag of conservatism and aggression that makes for tight scores and compelling finishes. Unlike the trends of the past few years teams are passing short to avoid turnovers through the air and running the ball to take advantage of defenses that are playing not to get beat on the deep pass. But when they fall behind late, offenses are airing it out and using all four downs to get back into games.
The numbers bear this out as well. We can see that in 2022 the median margin of victory for the winning team is smaller than it was in most seasons since 1991. Due in part to a lack of huge blowouts and lower scoring overall this season, teams are winning by an average 9.3 points, the smallest differential in more than two decades (2.3 points lower than average); no other single year had an average point differential under 10 points. Meanwhile the median margin of victory is seven points through Week 15, tying 2022 with 2015 and 2016 for the season with the tightest contests in that timeframe.
Overall, NFL teams are averaging 22 points scored per game headed into Week 16, down from an all-time high of 24.8 in 2020. But that doesn't mean the games haven't been thrilling to watch. Ask any fan the formula for an exciting game and they'd say having a close contest that comes down to the wire fits the bill. According to Hermsmeyer, teams are on pace to record 101 game-winning drives -- a cornerstone of a barnburner -- 12 more than in any other season since the merger. The NFL is averaging 5.5 game winning drives per week.
Leveraging work that Luke Benz has done for college basketball, I computed a Game Excitement Index (GEI) for every NFL regular season game since 1999 to help us identify just how many exciting games there have been this year compared to others.
This season has given us the third most exciting NFL season with an average GEI of 3.95. Only the 1999 season exceeded 4.0 GEI, but with playoff spots, home field advantage and first round byes still up for grabs in the closing weeks of the season we may see that number tick up before it's all said and done.
GEI is calculated by summing the absolute value of the win probability change from each play and multiplying by a normalized time parameter. This gives us an index by which we can rank each game’s excitement factor. The way to interpret, for example, a Game Excitement Index of 3.5 is that the win probability changed by a total of 350% over the course of that game. The higher the number, the more exciting the game.
As I've written before GEI is not a perfect metric, and it might fail to capture important aspects of the game that fans might find exciting -- it certainly weights OT games too heavily in my opinion -- but it does give us a sense of the flow of games and a way to quantify the excitement of NFL football.
If we look at the top-10 most exciting games so far this season a Week 1 overtime contest between the Pittsburgh Steelers and Cincinnati Bengals ranks as the most thrilling game with a GEI of 8.4. At least half of all games played in nine of the first 14 weeks of the season -- and all the games on the list below -- have been decided by six points or fewer. That's the definition of thrilling.
If you enjoy NFL advanced stats analysis like this check out my NFL Analytics website. And if you're like me and love win probability charts that resemble heart attack EKGs check out my NFL Win Probability Dashboard showing win probability graphs and GEI rankings of every NFL game this season.
This analysis was done using the nflverse suite of packages for R, a public repository of NFL play-by-play data and set of functions to efficiently scrape that data. It expands upon the features of nflscrapR and was created by Sebastian Carl, Ben Baldwin and Lee Sharpe. All code to generate the plots can be found on my GitHub page.
The formula is GEI=3600/t∑i=|pi−pi−1| where t is the length of the game (in seconds) and pi is the home team’s win probability on play i of the game. According to Luke Benz, “One can think of GEI as a measure of the length of the win probability curve if it were to be unwound, normalized to the length of a standard regulation game.” The reason he chooses to normalize the length of games is to remove the effect of overtime games that are otherwise sloppy being considered as more exciting than others simply because they lasted longer (thus had more win probability swings).