Expected Field Goal Percentage: A Better Way to Analyze Kicking

Michael Conroy / Associated Press

The number one metric used to evaluate kicking performances is field goal percentage.

In fact, it’s basically the only metric people use.

When a kicker walks onto the field to attempt a field goal, the broadcast flashes their FG%. That’s supposed to tell the viewers exactly how good the kicker is. Well, it’s not very good at that.

Field goal percentage only works under the misguided assumption that every field goal is equally difficult. The (obvious) reality is that longer field goals are less likely to be successful and certain kickers attempt longer field goals than others.

Let’s look at a real example. Chandler Catanzaro converted 80% of his field goal attempts this year. Stephen Hauschka connected on just 78.57% of his attempts. However, Hauschka attempted three more 50+ yard field goals. As a result, his Expected Field Goal Percentage (xFG%) was 80.64%, far lower than Catanzaro’s 87.78%.

xFG% can be compared to a player’s actual FG% to see if they are performing above (positive +/-) or below (negative +/-) expectation. Knowing this, we can see that both of the aforementioned players performed below expectation. However, Catanzaro’s differential of -7.78 is significantly worse than Hauschka’s -2.07.

xFG% is very simply calculated and can be improved upon — this is just a foundation. A player’s field goal attempts within the following ranges are recorded: 0-19 yards, 20-29 yards, 30-39 yards, 40-49 yards, 50+ yards. Let’s say a player attempts 10 field goals in the 40-49 yard range. We calculate that the league average FG% for these kicks is approximately 76.27%. Therefore, the player is expected to score on 7.627 of those 10 attempts. An expected number of makes is calculated for each of the five ranges. The total expected makes for the player is divided by the number of field goals they attempted to find their xFG%.

Going through this process yields these results for kickers with at least 20 attempts this year:

PlayerxFG%FG%+/-
Matt Bryant81.80%95.24%13.44%
Aldrick Rosas*87.49%96.97%9.48%
Jason Myers*82.77%91.67%8.90%
Robbie Gould88.26%97.06%8.80%
Wil Lutz84.88%93.33%8.45%
Dustin Hopkins82.27%89.66%7.38%
Josh Lambo84.35%90.48%6.13%
Justin Tucker+85.01%89.74%4.73%
Jason Sanders86.13%90.00%3.87%
Ka'imi Fairbairn84.51%88.10%3.59%
Harrison Butker86.48%88.89%2.41%
Greg Zuerlein86.09%87.10%1.01%
Daniel Carlson80.01%80.95%0.94%
Matt Prater86.59%87.50%0.91%
Adam Vinatieri84.73%85.19%0.45%
Ryan Succop86.80%86.67%-0.13%
Mason Crosby81.63%81.08%-0.55%
Greg Joseph85.73%85.00%-0.73%
Randy Bullock83.65%82.61%-1.04%
Sebastian Janikowski82.89%81.48%-1.40%
Brandon McManus81.52%80.00%-1.52%
Jake Elliott85.63%83.87%-1.76%
Stephen Hauschka80.64%78.57%-2.07%
Stephen Gostkowski87.56%84.38%-3.19%
Brett Maher84.01%80.56%-3.45%
Chandler Catanzaro87.78%80.00%-7.78%
Cody Parkey85.85%76.67%-9.18%
Dan Bailey87.17%75.00%-12.17%
Chris Boswell84.35%65.00%-19.35%

While this metric adds more context to the evaluation of kickers, it’s imperfect. Justin Tucker has the 6th highest +/- despite earning a spot on the First-Team All-Pro team. Why? Well, 50-yard field goals and 65-yard field goals (which Tucker attempted this season) are treated equally by xFG%. That shouldn’t be the case. Also, playing surface and weather have an impact on kicking. Of course, xFG% does not account for these external factors.

In any case, it’s certainly better than just using FG%.

You can view the full data from the past six years here.

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