How do NFL Kickers Age?

I was watching games on Saturday while chatting with another college football diehard. We were both enamored by the ongoing failure (relatively speaking) that is field goal kicking at perennial powerhouse Alabama. Juxtaposed against their otherwise prolific success, conjecture proceeded about the underlying cause(s) of ‘Bama’s FG kicking woes over the years. 

FG kicking troubles pervade the college game, frustrating fans, and our conjecturous chat led me to wonder if and how NFL kicking is better than college kicking. This led me to wonder if kickers just get better (or more consistent and reliable) as they get older. We did some Google searching but couldn’t find any NFL kicker aging curves for accuracy. So, we made our own.

Figure 1. Histogram of career lengths for NFL kickers 1960-2018

First, we obtained a bunch of NFL kicker data from the wonderful resource known as PFR. This includes season-by-season data for 369 NFL kickers from 1960 through present. These kickers made 33558 of 45777 FGs (73.3%) and 54658 of 56369 (97%) extra points. Based on the distribution of career-lengths shown in Figure 1, there were concerns that the large amount of kickers with 3 or fewer NFL seasons would skew the analysis. Our concerns were reinforced when we looked at Figure 2. 

Figure 2. Mean Field Goal % by length of career in seasons for NFL Kickers 1960-2018

Kickers with 3 or fewer NFL seasons have notably lower career FG% than kickers with lengthier careers. This in itself is not surprising, but it would confound the interpretation of the data. The lower accuracy of kickers with 3 or fewer seasons might lead to exaggerated year-to-year increases in accuracy in the early stage of the kicker career. To better convey this, displayed in Figure 3 is the average FG% in each season of the careers of kickers with ≤3 NFL seasons and >3 seasons. 

Figure 3. Mean FG% in each season for NFL kickers 1960-2018 with career lengths of <4 or >3 seaons

Figure 3 also suggests that FG% increases linearly as kickers age; as if kickers just keep getting more accurate. However, recall the smaller quantities of kickers with lengthier careers seen in Figure 1. The continued increases in accuracy by kickers with lengthier careers may be obscuring the declining accuracy in later seasons of kickers with shorter careers. This is exactly what is shown in Figure 4.

Figure 4. Thick lines are LOESS curves of the average FG% in each season of careers of NFL kickers 1960-2018 with various career lengths; fainter, thinner lines are raw mean FG% in each season

There is a group of ‘super agers’, kickers with careers longer than 16 years, whose annual FG% seems to level out and remain constant around their 12th season—which is about when kickers with careers of 11-16 seasons begin to experience slight declines in accuracy. Likewise, kickers with 11-16 seasons appear to peak around their 7th season—which is about when kickers with careers of 4-10 seasons start to decline.

Let us look at the data another way. Figure 5 contains average FG% through the course of the career normalized such that 0.50 (on the X-axis) represents a season halfway through the course of the kicker career. Figure 5 shows that, aside from kickers with ≤3 NFL seasons, NFL kickers start to experience a downward trend in accuracy about 75% of the way through their career. 

Figure 5. Career length is normalized such that 0.00 = rookie season, 1.00 = final season, and 0.50 = halfway through career. Thick lines are LOESS curves of the average FG% in each season of careers of NFL kickers 1960-2018 with various career lengths; fainter, thinner lines are raw mean FG% in each season

Summarily, the (slightly manipulated) raw data indicate that NFL kickers experience declines in accuracy late in their career (Figure 5). However, using the percent of the way through the career (as in Figure 5) does not conduce toward a prospective aging curve for NFL kickers. That is, a predictive model could not know beforehand how long a kicker’s career will be. In other words, future analyses will need to model an NFL kicker aging curve based on seasons in the League (or perhaps age). Future analyses should also account for era—FG kicking has improved dramatically over the years—and FG accuracy by distance. PAT% might also be informative (more so since 2015). Likewise, some measure of consistency (e.g., coefficient of variation) may provide a more alternative measure (than accuracy) of kicker performance.  

Icing the Kicker in NCAA Football 2005-18

In gridiron football, the icing the kicker phenomenon is thought to occur when the defending team calls a TO just before the ball is snapped on a FG attempt (FGA) that could tie, win, or otherwise sway the outcome of the game in favor of the kicking team. The motivation for calling the TO is that it could somehow disturb, or ‘ice’ the kicker in a way that he will be more likely to miss the FGA. 

Other authors have endeavored to examine icing the kicker. Some have reported that, in the NFL, calling a TO before a FG does not reduce the likelihood of making a FGA, whether controlling for FGA length or not. Other studies suggest suggests there is indeed an effect of reducing likelihood on longer NFL FGAs that is absent on shorter FGAs, when controlling for FGA length and other factors. At the collegiate level, it appears that icing the kicker may be effective on longer FGs; specifically, greater than 45 yards.  However, this study had a small sample of iced kicks.

Table 1. Descriptive Statistics for NCAA FGAs 2005-18

				This Many Attempted Fields Goals were
Quarter	FG%	uFG%	Attempted	Made	Blocked	Home Attempts	Last 2min Attempts	≤15s after TO Attempts
1st	0.727	0.753	7051	5123	248	3559	1129	468
2nd	0.702	0.732	11473	8049	475	5957	4413	3264
3rd	0.740	0.766	6629	4906	224	3417	1033	425
4th & OT	0.715	0.747	7176	5129	308	3764	1566	1754
TOTAL	0.718	0.747	32329	23207	1255	16697	8141	5911

We here at POTH sought to reexamine icing the kicker at the collegiate level using a much larger data set. This includes 32,329 FGAs from NCAA Division I FBS vs FBS and FBS vs FCS games from 2005 through mid-November 2018. Table 1 has the breakdowns of some data we’ll refer to throughout. The last 2 minutes refers to FGAs during the last two minutes of quarters 1 through 4 and any FGA occurring in OT. 

Let us start with blocked FGAs, though. Notably, as seen in Table 1, blocked FGAs were more likely to occur in the 2nd quarter and 4th quarter and OT (χ² = 12.3, p = 0.007)—the situations in games most relevant to icing the kicker. Longer FGAs were more likely to be blocked regardless of the quarter (p < 0.001). FGAs were also more likely to be blocked in the last 2 minutes of quarters and OT, but especially in the last 2 minutes of the 4th quarter and OT (p = 0.06). For these reasons, we shall include in our analyses only unblocked FGAs. This leaves 31,074 FGAs for analysis.

Table 2. Proportional Statistics for NCAA FGAs 2005-18

		Proportion of Field Goals Made
Quarter	% Blocked	Home Team	Away Tem	Last 2min	Before Last 2m	≤15s after TO	No TO Before
1st	0.035	0.743	0.710	0.731	0.751	0.726	0.752
2nd	0.041	0.714	0.688	0.677	0.746	0.680	0.742
3rd	0.034	0.755	0.724	0.743	0.765	0.701	0.769
4th & OT	0.043	0.728	0.700	0.676	0.754	0.694	0.749
OVERALL	0.039	0.757	0.736	0.725	0.754	0.721	0.752

About 74.6% of (unblocked) FGAs are made. Figure 1 shows that FG% declines as the length of the FGA increases. There is some variation in FG% between quarters, with 3rd-quarter FGAs being most successful. Only differences between the 3rd and 2nd (p = 0.03) and the 4th and 3rd (p = 0.03) are significant when we account for length of the FGA, which is, by far, the most significant predictor of FG success. Longer FGAs are less successful at all points in the game. 

Figure 1. Likelihood of Making a FGA, by Length (using binomial smooth)

FGAs by the home team (75.6%) are about 2.8% more likely to be made than FGAs by the road team (73.5%) (χ² = 17.9, p < 0.001). When controlling for FGA length and quarter, home FGAs are 6.6% more likely to be made (p < 0.001). However, this advantage of home FG% is relatively constant at all FGA lengths. That is, home-team FG kickers tend to be slightly more successful than road-team kickers on FGAs of any length, and at any point in the game. 

What about the FG% in the last 2 minutes of quarters, when icing the kicker usually occurs? Table 1 shows that it clearly drops in the 4th quarter and OT (in the 2nd too). This drop in FG% in the last two minutes is, however, diminished when controlling for FGA length, quarter, and home/away (p = 0.52). It should be noted that FGAs in the last 2 minutes of the 2nd and 4th quarters are 1-2 yards longer than FGAs at other times in the game (ps < 0.002). 

How do the stakes of the game effect FG%? The opportunity to tie the game seems to have a general effect of increasing the likelihood of making a FG (p = 0.05). Otherwise, though, there is no effect of stakes on FG% when controlling for length, quarter, home/away, and being in the last 2 minutes or not. 

FGAs 15 seconds or less after a TO are made 72.1% of the time whereas other FGAs are made 75.2% of the time (χ² = 24, p < 0.001). Now, this is just if any TO is called; that is, by the offense, the defense, or some other TO that was not attributed to either team in the data. Really, we have a variable that indicates whether the TO was called by the offense, the defense, was unattributed, or if no TO was called. If we were to continue the analysis as we have been doing it, we would examine a four-way interaction between quarter, last 2 minutes or not, stakes, and who called the TO before or not. Four-way interactions are messy. And three of those variables have four levels. We should do something else.

Figure 2a. FG% by TO Type

Figure 2b. FGA Length by TO Type

Let us narrow our focus to FGAs in the last 2 minutes of the 4th quarter and OT where the offense can either tie the game or take the lead with a FG, which leaves 2,173 FGAs for analysis. In the two figures we see that iced FGAs (i.e., those after a defense TO) [a] are the least successful, at ~70%, but [b] are also, on average, the longest FGAs in this game situation. Thus, when we model the likelihood of making a FG, while accounting for length and home/away, there is no effect of icing the kicker (p = 0.24). Like, icing the kicker has no statistically differentiable effect of decreasing the likelihood of making longer FGAs (p = 0.25). However, the estimated marginal probabilities in the figure below suggest that the likelihood of iced FGAs declines slightly more at longer distances, although, again, this is not statistically significant. 

Figure 3. Estimated probabilities of FG% by length and who called TO in last 2 minutes of 4th & OT

Whereas we have used the raw yardage value for FGA length, the one previous study of icing the kicker in NCAA football split length into ‘bins’: distances of 18-25 yards, 26-35 yards, 36-45 yards, and >45 yards. The author of the previous study used only data from 2017-18 and found that of 38 iced FGAs in the last 2 minutes of the 4th quarter and OT, only 26% were made. If I examine only data from 2017-18, I find these same numbers (38 iced FGAs, 10 made, 28 missed). Below, using all data, I went ahead and show the FG% for each of these length-bins by who called the TO, for the sake of comparison across studies. The quantities of FGAs are shown parenthetically. Longer iced FGAs appear to be made lower rates.

Figure 4. Proportion of FGAs Made by TO Type by Yardage Bins used in Dalen (2018)

Summarily, the present report examined icing the kicker in NCAA football. This study used a sizable data set which would enhance the generalizability of the findings. However, the primary analysis indicated there was no effect of icing the kicker. Additional examination suggested that there might be an effect of icing the kicker at FGAs longer than 45 but such a conclusion is limited by there being fewer FGAs attempted from these lengths (i.e., smaller sample) and the variability of success at increasing lengths. Likewise, other potentially influential factors such as meteorological conditions, team FG kicking/defensing quality, and on-field activity were not accounted for in the analysis. NCAA football coaches should continue utilizing icing the kicker so they may endure the rancor of punditry, boosters, delusional fans, etc., when their teams lose games on last-second field goals.  

Comparison of WNBA and NBA 2-Point Field Goals

Comparisons of WNBA and NBA Association-wide, season-level data were compared in a previous post. For the season of each that was analyzed, disparities in FG% were evident. Initially, the NBA appeared to be more successful shooting, 44.9% to 42.5%. However, when excluding dunks, the WNBA was slightly but significantly more successful, 42.5% to ~40%. Having recently acquired WNBA play-by-play (PBP) data for the 2016 season, we can more granularly analyze FG%.

All WNBA data were extracted from the regular season 2016 PBP which is available upon request. I will dump this data along with the regular season PBP data from the 2014 and 2015 seasons in a future post. The NBA shot type and assisted shot type data for the 2015-16 season was culled from Basketball Reference.

Table 1a. WNBA & NBA 2016 counts

Type	Total	NBA	WNBA
All 2FG	Made	75549	9815
	Attempted	153768	20676
Shots	Made	35680	4769
	Attempted	89487	12318
Layups	Made	30439	5045
	Attempted	53920	8356
Dunks	Made	9430	1
	Attempted	10361	2

Table 1a contains counts and 1b proportions of 2FGAs segmented by shots, dunks, and layups between the Associations for the regular seasons ending in 2016. The class of ‘shots’ includes not only jump shots but also others identified in the PBP as floaters, hooks, and runners. Table 1b also contains Chi-square test-statistics demonstrating that the is NBA is slightly more successful shooting 2-point shots. The WNBA is significantly more successful with layups. The Chi-square was not performed for dunks because only 2 were attempted by WNBA players.

Table 1b. 2pt-FG% by Shot Types

Type	NBA	WNBA	χ²	p
All	0.491	0.475	6.961	0.008
Shots	0.399	0.387	2.623	0.105
Layups	0.565	0.604	12.230	0.000
Dunks	0.910	0.500

So, these findings provide a more nuanced perspective on the findings from the previous post that WNBA is more successful shooting non-dunk shots. The two posts did employ data from different seasons. Nonetheless, given the present data, the two Associations ostensibly shoot 2-point shots (i.e., not dunks and layups) with similar successfulness—the p¬¬-value approaching conventional significance levels is likely a result of large quantities. That is, a 1 percentage-point advantage to the NBA may approach significance statistically, but I suspect it is ecologically meaningless.

Alternatively, the WNBA was significantly more successful than the NBA shooting layups. My initial notional hypothesis was that because males are predisposed to heightened athleticism, there are more contested or blocked layup attempts in the NBA. Ironically (to the impetus for pursuing this line of research), although blocked layup attempts can be extracted from the WNBA PBP, I am unable to locate blocked layup attempt data for the NBA (short of scraping). Likewise, contested shots attempted from with ≤5ft from the basket are available for the NBA but shot contesting is not recorded in the WNBA PBP.

Table 2. Assists on Dunks + Layups

Dunk + Layups	NBA	WNBA	χ²	p
Made & Assisted	15536	2749	6.596	0.010
Made	64281	8358
Proportion Assisted	0.242	0.329

Table 2 contains the proportion of dunks and layups, combined, that were assisted. The WNBA assists on significantly more of their successful layups (and dunks) than does the NBA. So, this might also explain why WNBA players are more successful executing layups. Because the WNBA assists on a greater proportion of layups, there may be more floor spacing or player movement such that defenders are less frequently positioned to defend layup attempts. Indeed, this notion is interrelated to there being greater athleticism in the NBA, as well as greater size, and thus less floor space in the NBA. Lastly, WNBA players may execute successful layups in certain scenarios whereas NBA players would likely execute successful dunks such as on uncontested fast breaks.

If athleticism were the sole determinant in explaining WNBA layup FG% superiority, there would be little recourse for defensive strategists other than playing larger or quicker players. However, if it is the result of floor spacing or player movement, I suspect WNBA coaches transiently employ zone defenses to narrow passing and driving lanes to reduce opponents’ layup success.

Summarily, this report indicates that WNBA and NBA players shoot with similar accuracy on 2-point FGs that are not dunks or layups. Also, the WNBA is more successful on layups than the NBA. This author posited three reasons why this may be: (a) greater athleticism and size on NBA players results in more contested layup attempts and passes; (b) relatedly, the WNBA assists on a higher proportion of its layups which may be the result of more floor space or player movement, but is also related to point (a); and, (c) NBA players may execute dunks in many scenarios where most WNBA players would have to execute layups, also related to point (a).

2016 WNBA FG% Distribution by Shot Location

So this is a first for POTH: I am posting twice in one day, or twice in one uninterrupted span of wakefulness. However, it is a diminutive post. Below is a shot chart for the 2016 WNBA regular season with field goal percentage. Greener means higher shooting percentage, navy-er means lower percentage. 

Rather unexpectedly, I was able to amass some data rather quickly and it happened that the shot locations were included. I got excited as I often do when there are discoveries at 00:18. Of course, the chart would would be more informative if the hexagons were sized according to the quantity of shots therein (e.g., here). But it's late and my belly aches.

Chart 1: Distribution of FG% by Shot Location in the WNBA, 2016

A previous post revealed some differences in WNBA compared to NBA league-wide aggregate statistics. I am now able to address these and other topics.