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.  

Punt Returner Personalities: The Enterprising Risk-Taker, the Dependable Risk-Averter, and the Consummate-Moderate

Let us examine how the frequency with which punt returners produce negative yardage can be viewed as a sort of personality trait. Moreover, we’ll examine how such a trait can provide insight into on-field performance. The data set (initially) includes 19,363 punt returns from 2002-18 NFL seasons, both regular season and playoffs. We’ll examine the career data of punt returners who spent at least one season as the primary returner for a team. Without snap count data for the whole data set, I defined primary returner as anyone who returned the most punts (plus fair catches) for a team in at least one season; within-season ties for a team were permitted (i.e., could have more than one primary returner from one team in a season). That totaled 227 punt returners, with a range of 8 to 331 career punt returns (only, not fair catches). I then excluded returners with 30 or fewer career returns to have a decent sample size of returns for each returner. This leaves 170 primary returners who, together, returned 16,234 punts and have a median of 79 career returns (25th percentile = 47; 75th = 119). 

The first step was classifying returners based on tendency for negative yards. I started with the proportion of career returns for negative yards based on the findings of the previous post. My criterion for negative return yardage is ≤ -2, excluding returns with muffed catches. I set the threshold at -2 because I felt that returns of -1 yard could occur inadvertently, whereas ≤-2 yards are more likely the result of volitionally moving into the negative. Then I binned returners into three groups using cutoffs at the 33rd and 66th percentiles, or 2.1% and 4.43% of career returns being negative, respectively. I thought this segmentation would provide three groups of risk-preference: risk-averse, moderate, and risk-takers.
Several variables were selected to examine how this conceptualization of risk-preference might relate to on-field performance. For each returner, I computed the following variables to explore relationships between risk-preference and on-field outcomes.

• % of career returns >6 yards 
• % of career returns with a TD
• % of career returns + fair catches that were fair catches
• % of career returns where the returner muffed the catch
• % of career returns where the returner fumbled the ball
• % of career returns where there was an illegal blocking penalty called against a member of the return team

Figure 1. Median career punt returns by risk-preference group

Figure 1 shows that moderate returners have the highest median number of career returns, followed by risk-takers and then risk-averters. One possible explanation is that guys with fewer opportunities to return punts may be more averse to risk, perhaps, in hopes of securing roster spots. There is some potential evidence for this assertion in the data. Returners who were ever a primary returner were less likely to call for a fair catch (32.7%; 7900 of 24129 returns and fair catches) than those who were never a primary (35.3%; 1710 on 4844), χ² = 11.9, p < 0.001. That is, I’m saying that guys who are less experienced returning punts may be more cautious.

Figure 2. For visualization, I split returners into groups above and at or below the median of 53.5% of career returns being >6 yards (2 groups). I split returners into groups above or at and below the median of 1.18% of career returns with a TD (2 groups).

Figure 2 indicates how likely a returner in each risk-preference group is to return for more yards than would be expected by chance alone and return for a TD. Indeed, compared to the risk-averse, moderates (p = 0.02) and risk-takers (p < 0.001) returned a higher proportion of their career punt returns for TDs. Likewise, compared to risk-takers, the risk-averse (p < 0.001) and moderates (p = 0.04) returned a higher proportion of their career returns for >6 yards. If we exclude negative returns and returns for TDs and look at the % of returns >6 yards, the difference between risk-averters (57.9%) and risk-takers (54.7%) is significant (p = 0.01); but moderates (54.8%) are no different than risk takers (p = 0.43).

Figure 3 shows probabilities and standard errors of other variables by risk-preference. Moderates (p < 0.001) and the risk-averse (p < 0.001) had a higher proportion of fair catches than the risk-takers. This suggests that risk-takers were less likely to call for a fair catch, but this is largely my own conjecture as we cannot account for whether returners had more punts out of bounds, downed, declared dead, or touchbacks. Also, we cannot account for how often returners returned a punt when they should have called for a fair catch. 

Figure 3. Proportion of career punt returns are fair catches, muffed, fumbled, or had a holding-type penalty, by group.

Compared to the risk-takers, the risk-averse had significantly fewer returns with penalties (p = 0.005), and there was a similar trend for the moderates (p = 0.12). This finding is potentially due to some quality of risk-takers because the results are essentially unchanged if we control for number of career returns, career average return yards, and career touchdown return %. Likewise, using all of the data, penalties are called less often on negative returns (9.9%; 7 of 720) than positive returns (12.3%; 2292 of 18643), χ² = 3.83, p = 0.05 (penalties enforced and declined are included).

There were no significant group differences in the proportion of fumbles (ps > 0.24) and muffs (ps > 0.32). If we control for the number of career returns, average yards, and TD%, the risk-averse tend to have fewer fumbles than the moderates (p = 0.13) but otherwise, the proportions of fumbles and muffs are unchanged. 

There is a shortcoming of my thesis to consider. I am assuming that returners who are more often tackled for a loss of ≤-2 yards (i.e., negative returns) on returns are also more likely to run into the negative area overall. Based on the available data we cannot determine if this is the case. It may be that the risk-averse and moderates run into the negative just as often, but the risk-takers just are more likely to be tackled after running into negative return yardage space. A caveat to this is that risk-taking returners tended to be less likely to call for fair catches. However, only if we have data indicating that the risk-takers are more likely to forgo fair catches when the coverage unit is closing in on them can it be demonstrated that they are more likely to take risks.

Importantly, these findings show that there appears to be a balance to productive punt returning: Risk-takers may produce more TDs, but they also produce return yardage less consistently, whereas risk-averters may produce return yardage more dependably, they also produce fewer TDs. Ultimately, punt returners who take risks in moderation are probably the most productive in that they consistently produce decent return yardage while still producing TDs at a relatively high rate.


Methods 
We used generalized linear models (GLMs), specifying Poisson distributions, to compare on-field outcomes between the risk-preference groups. There were six GLMs. The dependent variable was the quantity of career returns with a given outcome, for each returner. The independent variable was risk-preference. The DV was offset by the total career punt returns (or punt returns + fair catches for the model of fair catches, this yields a proportional value. The variables are described below.

• The proportion of career returns >6 yards. I used >6 yards because 7 is the median of 90% of the punt returns in the data (range of -1 to 32) and it is a decent guess at the return yards we would expect to occur randomly. Then I split returners into groups above and at or below the median of 53.5% of career returns were >6 yards (2 groups). In other words, returners with a lower proportion of returns >6 yards are more often returning punts below what we would expect based on chance alone.
• The proportion of career returns with a TD. I split returners into groups above or at and below the median of 1.18% of career returns with a TD (2 groups). My thought was that risk takers should return TDs at a comparable rate as the other groups, despite having more negative returns.
• The proportion of career fair catches, which is the number of fair catches divided by the sum of fair catches and returns. Ideally, the number of fair catches would be divided by the number of punts on which the returner was on the field to return the punt. Nevertheless, the thought here is, risk-takers should be less likely to call for a fair catch overall. 
• The proportion of career punt returns where the returner muffed the catch. I included this as a measure of conscientiousness. That is, can the returner do the most critical and fundamental part of successful punt returning: catch the ball?
• The proportion of career punt returns where the returner fumbled the ball on the return. I included this as another measure of conscientiousness, perhaps, although fumbles tend to be random events. 
• The proportion of career punt returns where there was a block in the back or illegal block called against a member of the return team. 

Returning Punts and Losing Field Position

Fans of collegiate and professional football teams have seen it. The opposing team punts. Arrival of the coverage unit is imminent as your return man situates to catch the ball. He shows nary a handwave, telling everyone there will be no fair catch on this punt. No, yours is an enterprising returner. Upon catching the punt, he will begin to explore the prospect of negative return yardage whilst attempting to evade the coverage unit. Perhaps, he will pick up some punctual blocks from his teammates or move quickly enough to elude would-be tacklers before reaching open grass and improving field position for your offense. Sometimes this risk produces minimal gains and on other occasions, the returns are huge. Yet, to the displeasure of fans and the hypertension of coaches, sometimes many yards are lost, and offenses start drives closer to their own endzone. 

There are other ways that field position is lost. I’m less interested in these, but we can examine them too. Punt returners can muff the catch or fumble the ball during the return. Although neither muffs nor fumbles guarantee lost field position, both create a risk for lost field position. Moreover, both risk turnovers--let alone the detriment to field position. Penalties. Specifically, the holding, block in the back, and clipping varieties, which can negate returns and start the offense closer to their own endzone. 

Who is to blame for lost return yardage? The ability of the return team to pressure the punter and the extent to which the coverage unit protects the punter. The skill of the punter to both focus and execute as well as the distance (and hangtime) of the punt matter, too. It is the punt returner who chooses to run toward his own endzone. It’s also on him if he muffs the catch, and he needs to protect the pigskin to prevent fumbles. Penalties just suck, I'm sorry. Nonetheless, regardless of how it occurs, lost field position is created by an interaction between individual players and their emergent units. One simple way we can look at who is responsible for lost field position on punt returns is intraclass correlations (ICCs; though my methods differ).

Our data are (primarily) 19,363 punts that were returned during 2002-18 NFL seasons (regular and some playoffs; holding-type penalties included). We include in the model return teams and coverage units both by season and across seasons to account for seasonal personnel changes and season-to-season consistency, respectively. Season itself was included to account for League-wide fluctuations in gameplay. In each model, we shall also account for the line of scrimmage and the punt yards. 

Table 1. ICCs of Team Units for ways Field Position is Lost on Punt Returns in NFL, 2002-18

	On Punt Returns				All Punts
Unit	Negative Yards	Muffs	Fumbles	Penalties	Penalties
Returner	0.059	0.042	0.028	0	NA
Return Team by Season	0.001	0.014	0	0	0
Punt Team by Season	0.021	0.025	0	0.001	0.004
Punter	0.012	0.008	0	0	0
Return Team in all Seasons	0	0.001	0.006	0.001	0.001
Punt Team in all Seasons	0	0.005	0.008	0	0
Season	0.005	0.019	0	0.006	0.007
Unit R² (sum of ICCs)	0.098	0.114	0.042	0.009	0.012
Line of Scrimmage & Punt Yards R²	0.043	0.092	0.004	0.019	0.073
Total R²	0.141	0.206	0.046	0.028	0.085

Across all seasons, 721 non-muffed punts were returned for negative yardage, or 3.72% of returns, with an average of -3.53 yards (SD = 2.25). Table 1 contains ICCs for each unit. The ICC value means that 5.94% of negative yardage is due to some qualities of punt returners, 2.11% is due to some qualities of the punting teams, and 1.23% is due to the punter. In other words, the ICCs can be summed to obtain an approximate R². The effect of punting team is not statistically significant (p = 0.32) but the effect of punter tends to be (p = 0.07), and the effect of returner is (p < 0.001) (compared to models with each excluded). Together, the remaining factors account for 0.54 %. That only 9.82% of the responsibility for negative returns is meaningfully explained speaks to the stochastic nature of punt returns and special teams in general. 

Unsurprisingly, returners bare the most responsibility for muffs. However, the punting team and the return team appear to contribute to this meaningfully as well. Returners appear to be mostly responsible for fumbles. Penalties appear to be mostly random based on the ICCs all being < 1%. 

Summarily, the present report showed that punt returners carry the most responsibility for negative return yardage, but qualities of the punting team and punter are likely involved. Conceivably then, some punt returners should be more likely than others to have returns for negative yardage. In other words, a subset of returners may attempt to evade tacklers despite the risk of compromising field position for their offensive units. How such a tendency relates to punt return outcomes (e.g., yards gained or touchdowns) is a matter for future study. 



Methods
For analysis we’ll use generalized linear mixed models and specify binomial distribution. Essentially, we are estimating the likelihood that there is a return of negative yards, a muff, a fumble, or a penalty on a given punt and how much of that can be attributed to returners, punter, return teams, coverage units, and the season. Return teams and coverage units were examined by season and overall to account for seasonal personnel changes and season-to-season consistency, respectively. Season was included to account for League-wide trends in gameplay. We also include the punt spot and punt yards. For each GLMM, we'll use the icc() function of sjstats package in R to compute ICCs.

Bulleted below are definitions for each of the ways field position is lost by punt returners and units. 

• I define negative returns as returns of ≤ -2 yards on an attempted return without a muff. Muffing should be should be considered separately from a decision to run into negative yardage. I set the threshold at -2 because I felt that returns of -1 yard could occur inadvertently, whereas ≤-2 yards are more likely the result of volitionally moving into the negative.
• Muffs occur when the returner botches the catch. Muffs do not necessarily result in lost yardage, but they risk lost yardage and turnovers.
• Fumbles occur when the returner loses possession of the ball during the return. Same caveats as muffs.
• Penalties are holding, block in the back, and clipping penalties committed by the return team. We’ll look at penalties with and without considering returners, that is, on punt returns only (i.e., 19363 punts) and then on all punts (i.e., 41912 punts). This is because the play-by-play data only tell me when a returner was on the field for punt returns and fair catches and so we exclude returner from the model with all punts. 

How Meteorological Conditions Affect Punting and Punt Outcomes

How does weather affect punting and punt outcomes? We know from prior studies that decreasing temperature is associated with reduced accuracy for field goals from the 25-yard line and farther.  Likewise, longer field goals tend be more accurate in the high altitude of Denver.  Regarding punts, there is evidence suggesting wind reduces punt yards. 

In short, we’re using 37,253 or so NFL punts from 2002-16. A weather data set culled from NFL Savant covers only 28,000 or so of those punts, through 2013, or about 75% of the data set. 

Figure 1. Average Punt Yards by Altitude

We can first see in Figure 1 that altitude has a limited effects on punt yards (PY) with the exception of the highest altitudes. The second highest altitude group includes Atlanta and Arizona, which average nearly 1 yard more on punts (p = 0.001; Atlanta is a dome) and Denver averages nearly 3 yards more per punt (p <0.001). This is consistent with findings on field goals. 

I used a generalized additive regression with smoothing splines to examine weather effects on punting. The punt spot (PS), wind (in MPH), temperature (Fahrenheit), and precipitation (%, 0-1) as well as all interactions between the meteorological variables were all fit with splines. I included the categorical variable for altitude instead of a smooth line for altitude because Denver distorts the altitude spline. I suppose I could have transformed the variable, but the laziness vice is king for the day. I also included a variable indicating if the punt was in a dome or open stadium. 

Figure 2. Modeling punt yards as a function of temperature, precipitation, and wind

As shown in Figure 2, weather appears to influence punt distance. Lower temperatures result in shorter punts. Wind appears to be most influential when precipitation is greatest. Maximum precipitation appears to reduce punts by about 3 yards, on average, compared to no precipitation. The influence of temperature is diminished when wind and precipitation increase. That punt distances are reduced in increasingly inclement meteorological conditions is consistent with the existing literature on field goals and punts in the NFL. The effect of the Denver altitude is consistent in this model, but the effect of Atlanta and Arizona is diminished likely because the model accounts for dome conditions. The upper rightmost panel is weird, though, perhaps because having only a few cases with higher wind speed influences this finding?

Figure 3. Punt return yards by altitude

There appears to be negligible effects of altitude on average punt return (PR) yards on punts that were actually returned (R2 < 0.001, that is R-squared not p!); see Figure 3. Not shown is an ecologically meaningless but statistically significant effect of temperature increasing PR yards on returned punts by about 0.13-yard for every 30° increase in temperature. Ah!, the frivolity that emerges from large data sets.

Figure 4. Punt outcomes by altitude. dd = defense downed/declared dead. fc = fair catch. oob = out of bounds. pr = punt return. tb = touchback.

It appears that there are more touchbacks in Denver, χ² = 92.15, df = 28, p < 0.001. Not much else to say here.

Figure 5. Secondary punt events by altitude. blk = blocked/tipped punt. fum = fumble. muff = returner muffed catch. pen = holding, blocking in back, or clipping penalty on return team. td = touchdown.

There appears to be more penalties in Atlanta and Arizona, but I am unsure why this is. Arizona had six seasons with 5 or fewer wins from 2002-13. ATL had three such seasons. All-around poor team play could have evidenced in more block in the back type penalties on punt returns. 

Figure 6. Punt outcomes as a function of temperature.

I used binary logistic regressions to assess the probability of several punt outcomes associated with several meteorological variables. The meaningful differences (to me) for outcomes due to temperature are between 25° and 75°. Specifically, there is a 5% greater probability of punts being declared dead or downed by the defense (DD) as it gets colder and 5% greater probability of punts being returned when it is warmer. 

Figure 7. Punt outcomes as a function of wind

For wind, I’m looking at the probability difference between no wind and 20mph. The probabilities of fair catches (FCs) decrease and DDs increase as it gets windier. This suggests to me that returners are less likely to even attempt to field the punt when it’s windier. OOBs also increase when it is windier. 

Figure 8. Punt outcomes as a function of precipitation

For precipitation, I’m looking at the change from none to maximum where there is a 5% less probability of a FC when it’s wetter, a 5% greater probability of TBs when it’s wetter, and a 5% greater probability of DD when it’s wetter. Together, these amount to there being fewer punt returns in wetter weather.

In short, the probabilities shown in Figures 6-8 demonstrate to me that punt returners are less inclined to even attempt catching a punt in colder and wetter conditions, and rightfully so. I’m unwilling, however, to conclude exactly the same for windier weather because [a] there are interactions between the meteorological variables not accounted for in these analyses; [b] the analysis accounts for the direction of neither the wind nor the punt; [c] steady winds and, more so, powerful wind gusts could dramatically alter the trajectory of a punt, and leave a return man far out of position. However, as shown above, windier, colder, and wetter conditions reduce punt distance meaning that the coverage unit is approaching the returner much quicker. 

Then I identified 7, 6, and 9 classes, respectively, for temperature, wind, and precipitation using an estimation-maximization procedure. I used these classes to examine the probabilities between meteorological variables and several secondary events: blocks, muffed catches, fumbles, penalties, turnovers, and TDs.  There was no difference in the distribution of PR TDs, fumbles, or turnovers between the classes of any meteorological variable (not shown). 

Figure 9. Muffs as a function of temperature and wind

For muffs, see Figure 9. It appears there is no difference in the distribution across precipitation (χ² = 12.1, p = 0.15; not shown) but the distribution does differ across wind (χ² = 15.9, p = 0.007) and temperature (χ² = 30.92, p < 0.001). Specifically, muffs increased in windier and colder conditions.

Figure 10. Blocked/tipped punts as a function of precipitation and wind

Shown in Figure 10 are blocked punts, which I’m wary of even broaching since it is such a rare event. There is no difference for temperature but there is a difference in the distribution across wind and precipitation. Blocks appear to be slightly less random when it is windier and wetter, but this could be due to adverse conditions affecting punt trajectory or increased pressure due to the expectations that punting is complicated by such weather conditions. However, we must be mindful that there are fewer samples at the meteorological extremes and the results very well could be spurious.

Figure 11. Block in the back, holding, or clipping penalties on the return team as a function of temperature

Distributions of block in the back, holding, or clipping penalties on the return team are no different for wind and precipitation. However, the distributions do differ across temperature such that penalties become more likely in warmer temperatures (χ² = 23.4 , p < 0.001). Penalties likely increase as temperature increases not because of some pressure exerted by warmer conditions per se but, rather, because punt returns are more likely as temperature increases. The odds of a penalty occurring on a punt that is returned are 4.6 times greater than on a punt with no return (z = 26.7, p < 0.001) whereas the odds of a penalty increase by about 0.004 for 1° increase in temperature (z = 3.03, p = 0.002), or by about 0.12 for an increase of 30°.

Summarily, very high altitudes increase punt yards. Colder, wetter, and windier weather reduce punt yards. There is a negligible influence of meteorological variables on punt return yards of returned punts. Punt returners, I subsume, are less likely to attempt to catch a punt during inclement weather. Fumbles, turnovers, and TDs appear to be stochastic and independent of the influence of meteorological conditions. Muffs, however, do appear to increase when it is colder and windier but not in greater precipitation. It seems blocked punts are slightly less random as precipitation and wind increase but these are the rarest of rare events. Penalties are slightly more likely to occur as temperature increases but this is likely due to there being more punt returns in warmer weather. So, that covers meteorology and punting with a healthy dose of chart gluttony. 

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.  

Field Position Part II

In a previous post I discussed how INTs and INT return yardage influenced starting field position (SFP). I will extend that discussion to include each of the other events that directly result in SFP: turnover-fumble returns, kick and punt returns, and missed field goals by opponents. As an aspiring defensive back, I of course took great care discussing interceptions. I will devote little discussion here to fumble recoveries and missed field goals. I will harp on kick-off returns but refrain from discussing punt returns at any depth.

Let me first state that my play-by-play (PBP) data differs slightly from the official record. I excluded yardage gained on returns for TDs in the analysis because a TD precludes SFP. Excluded also was return yardage gained prior to a turnover-fumble.

Concerning INTs, I emphasized that ending opponents’ possessions is most salient and that INT return yardage is a somewhat superfluous stat. INT return yards may be useful to compare playmaking abilities between DBs, although statisticians, teams, and observers might be better served knowing the SFP that resulted from an interception. This notion is definitely applicable for fumble returns where, again, the ending of opponents’ possession is most salient.

Likewise, it is also relevant for rating punt returners. For instance, a player fair catching a punt at his own 9-yard line would be recorded as a fairly unremarkable zero yards (i.e., it is counted in his average PRY). However, the fair catch was probably initiated in the presence of proximal defenders who could have disrupted the impetus of the punted ball at say, the 2-yard line had the returner declined to fair catch. Thus, by fair catching—despite accruing zero yards—the returner in the example would improve his team’s SFP by 7 yards (of course, the defense downing the ball is hypothetical).

The foregoing notion of field position in lieu of yardage is applicable to kick returns as well. For example, let us review the 2014 NFLleading kick-returners by average yards per return. I have Bruce Ellington of the 49ers at 24 returns for 25.9 yards per return;^c.f. he ranks about ninth in KR yards. However, Ellington gives his offensive teammates an average starting FP at the ~23-yard line—18^th on my list of qualifying players. It may be poor decision making on his behalf or poor block execution behalf of his teammates or that he generally fields kickoffs from superior kickers but we must acknowledge Ellington’s average catch-spot (CS) on KRs was nearly 3-yards into the endzone, ranking third-deepest on my list of qualifying players.¹

Although this post is about SFP, the above anecdotes underscore the entanglement of variables involved in appraising performances with yardage accrued. However, Ellington still gained those yards. If we are comparing players (or even coverage units), perhaps, Ellington does rank ninth in KR yards. However, football is about team success and on a given drive, a team is increasingly inclined to success the closer it begins to its opponent’s endzone. Conversely, Ellington’s team did start 3 yards closer to the endzone then would result from him taking more touchbacks.

Moving on, for all teams in the 2014-15 NFL season, I obtained all non-TD turnover-fumble returns, interceptions, kick and punt returns, and field goals missed by opponents using the Pro-Football Reference PBP searchtool. Opponents’ missed FGs include blocks but excludes blocks returned for TDs. For all plays except opponents’ missed field goals, I extracted [a] the spot of the INT, fumble recovery, or catch and [b] the spot at which the player was downed following the return. Computed with those values were [c] return yards or 20 for a touchback and [d] the SFP of the player’s offensive teammates. SFP was scaled such that teams’ own goal lines equaled zero and opponents’ goal lines equaled 100; greater yards indicate better SFP.

Table 1. Counts, Average SFP, and Average Return Yards for Events Resulting in SFP, NFL 2014-15

TEAM	TOTAL EVENT COUNTS						AVERAGE STARTING FIELD POSITION BY EVENT							AVERAGE RETURN YARDS BY EVENT
	KR	PR	FR	INT	oMFG		SFP	KR	PR	FR	INT	oMFG		KR	PR	FR	INT
KAN	68	76	5	5	5		29.3	25.4	28.7	22.4	44.0	23.6		25.4	8.6	0.0	15.2
CIN	78	74	5	19	5		30.3	24.7	30.3	27.4	50.8	26.0		24.9	8.4	0.0	9.2
NWE	68	64	7	16	5		30.6	22.7	32.0	36.9	51.8	29.6		22.1	7.5	0.4	11.7
DAL	75	66	12	16	2		28.9	21.1	26.9	35.5	43.3	21.0		22.3	7.3	2.6	9.3
TAM	81	63	11	11	7		26.6	20.7	25.3	30.1	48.8	32.6		21.6	7.2	1.3	6.9
IND	79	88	13	11	4		28.7	22.5	28.4	30.3	43.2	24.0		24.3	7.0	2.6	8.7
BAL	68	73	12	10	6		28.7	23.3	30.4	32.9	48.2	27.8		22.9	6.9	4.3	9.1
JAX	92	74	12	5	4		25.7	22.1	21.5	24.3	51.0	31.8		21.9	6.4	0.8	13.0
PHI	83	85	16	9	5		30.0	22.9	28.8	33.8	41.6	23.4		20.9	6.2	6.3	6.2
MIN	73	74	4	11	6		27.3	25.0	25.5	15.3	49.0	30.3		21.9	6.2	0.0	8.2
STL	79	74	11	10	1		28.0	22.1	28.8	32.4	42.1	35.0		22.9	5.9	3.3	10.8
BUF	71	86	8	18	7		30.2	21.3	27.0	25.5	60.4	26.9		20.6	5.9	4.4	19.2
OAK	94	81	4	9	5		24.2	19.9	24.7	26.0	61.9	24.2		21.4	5.7	5.3	8.4
CHI	97	49	8	13	6		25.9	21.1	27.0	15.9	43.6	23.8		20.5	5.6	2.4	10.9
SDG	81	66	8	6	2		26.4	21.3	26.5	24.1	31.7	33.5		21.1	5.5	0.0	12.0
SFO	70	74	5	21	0		27.8	22.6	28.3	16.2	48.0	-		22.9	5.5	0.0	18.8
ATL	89	55	7	15	4		26.5	22.4	25.7	32.3	40.5	27.5		22.5	5.4	5.6	6.9
MIA	82	57	10	11	6		31.1	24.2	25.5	21.3	50.6	23.5		23.9	5.3	0.0	17.1
DEN	75	84	5	16	5		28.9	22.6	28.2	26.0	54.4	25.2		21.4	5.3	0.4	10.8
TEN	89	72	6	11	5		25.8	23.2	24.8	38.8	52.7	31.0		22.5	5.2	7.2	10.8
ARI	76	77	5	15	4		26.9	19.6	24.8	20.8	51.3	32.5		20.2	5.1	1.8	10.3
NYJ	85	79	7	6	5		27.8	22.5	26.6	31.4	35.0	24.0		22.1	5.1	0.3	9.0
PIT	86	66	10	7	2		25.7	20.7	24.9	32.5	48.9	29.5		21.1	5.0	3.9	18.1
NYG	87	74	9	16	2		28.2	20.7	23.7	31.6	62.1	24.0		21.1	4.9	1.5	16.6
GNB	79	60	7	15	1		28.5	20.1	27.0	37.4	54.7	29.0		20.3	4.8	0.0	15.2
CAR	83	69	13	10	4		27.7	21.8	25.5	32.5	45.2	25.8		21.0	4.5	2.8	19.0
SEA	62	81	9	11	2		30.5	22.4	27.7	29.0	58.2	32.5		21.4	4.2	0.0	14.2
CLE	72	83	7	18	3		26.8	22.6	24.9	27.7	54.1	34.0		22.8	4.2	4.9	14.4
WAS	85	80	9	6	3		25.1	21.4	22.7	29.0	45.5	18.0		20.8	4.0	2.1	5.0
HOU	71	82	10	16	2		27.7	20.4	23.5	31.9	60.6	26.5		20.7	3.8	8.7	16.6
DET	70	81	7	18	4		29.9	21.1	29.4	32.9	59.2	27.3		21.8	3.8	1.8	18.7
NOR	86	62	6	12	0		25.5	22.2	22.0	18.5	42.9	-		22.3	3.0	0.0	12.5
League	Event Counts						Average Field Position by Event							Average Return Yards by Event
AVG	79	73	8	12	4	AVG	27.9	22.0	26.5	29.1	50.5	27.2	AVG	22.0	5.6	2.3	12.3
SD	9	10	3	4	2	SD	1.8	1.4	2.5	6.4	7.6	4.2	SD	1.3	1.3	2.4	4.2

Table 1 contains 2014-15 distributions, NFL team average SFP and yards gained for each event, and League averages thereof. KRY and PRY are computed with touchbacks equal to 20 yards and no return equal to zero yards. Neither New Orleans’ nor San Francisco’s opponents missed FGs, apparently. There is nothing particularly noteworthy in the table, otherwise.

I also can tell you several things. INTs have the largest impact on the next-SFP when statistically controlling for the initial play spot, the spot at which an INT, fumble recovery, or kick/punt catch occurred, and the yardage gained on the return.² I can also tell you that for all NFL teams, the majority of SFP yardage is derived from either KR yards or PR yards. Table 2 provides some insight into why this is.

Table 2. Characteristics of NFL Based on Majority of SFP

	Majority of Team SFP From
VARIABLE	KR	PR
Teams Count	11	21
avg SFP	27	28
avg SFP Unproductive Drives	24	24
avg KR-SFP	22	22
avg Unproductive Drive Yards	17	16
avg Punt Yards	45	45
Opp avg Punt Return Yards	9	9
avg Def. SFP After Unproductive Drive	24	23
Opp avg Unproductive Drive Yards	16	16
Opp avg Punt Yards	45	45
avg Punt Return Yards	5	6
% All Drives Turnovers	14%	11%
Opp % All Drives Turnovers	12%	12%
% All Drives End w/ Score	32%	35%
Opp % All Drives End w/ Score	39%	32%
win%	35%	58%

NOTE: Unproductive drives are defined as those that end without a score.
Scoring drives are those that ended in TDs or FGs.

In Table 2 we see that the two types of teams perform similarly in most situations. Notably, teams whose majority of SFP is derived from KRs commit TOs slightly more frequently. As an aside, this might suggest that while essentially random, a modicum of TOs may be attributable to offensive ineptitude (albeit, in single season sample). Those teams’ opponents also end drives by scoring considerably more frequently—23% more—than teams whose majority of SFP is derived from PRs. The PR-teams score slightly more frequently.

Most striking in Table 2, though, is the disparity in win percentage. The KR-teams can be expected to win 5.6 games whereas PR-teams can be expected to win 9.3 games. Thus, I conclude that, despite the indelible impact of Devon Hester or the ’84 Seahawks’ 3-4 monster, ultimately, SFP is largely the result of an ungenerous defense supplemented by relatively consistent and careful offensive play.

Summarily, the impact of various events on starting field position was examined using data from the 2014-15 NFL season. Although INT yards are most impactful on SFP in isolation, when statistically controlling for event-spot and return yardage, the majority of SFP is derived from either KR or PR yards. Likewise, winning teams garner most of their from PR yards. I concluded that this effect is likely due to defensive stops and consistent, careful offensive play.

---

¹ Minimum 1 KR per game scheduled.

² To accomplish this, SFP was regressed on to play start spot, event spot, and yards gained. The residuals were saved. An ANOVA was performed with those residuals as the dependent variable and event type as the independent variable. A significant effect of event type was found, F(4, 5641) = 17.422, p < .001. Roughly, planned post hoc comparisons indicate the effect of event on SFP could be ranked as INT > FUM > PR > MFG > KR.