Player Spotlight: Kickers!

Firstly, I want to thank those of you that actually started to read this even after reading the title. I promise there is fantasy information here! Second, I want to admit up front that I have a bias against kickers for fantasy football. Despite this, I wanted to see if that bias was real or imagined.

After a lively debate on the Fantasy Life App with a couple of users, jay dondon in particular, I wanted to go do some in-depth analysis on whether there was a real strategy involved, or if it was indeed a crap-shoot. I spent more time with kicker stats than I ever thought I would, and here is what I found. The points scored in all data is NFL points since fantasy scoring in leagues is different.

Home vs. Away

Being in the comfort of your own stadium with your own fans cheering you on has to count for something, right? This was one of the first things I looked at when creating this overly large Excel spreadsheet.

The difference in average between the two is statistically insignificant, especially if you add in that the standard deviation is almost 4 for both home and away splits over the course of the season. Basically, the majority of kicker performances can be expected to be between 3 and 11 points for home and away games. This actually is what the league average is overall.

Indoor vs. Outdoor

This one I was definitely interested in to see if there was any positive correlation to kicking indoors, as there is much less interference conditions wise that would effect the kick after it leaves the kicker’s foot. Much in the same is found here as well.

Surprisingly, outdoor had a slight edge. Again, the standard deviation was approaching 4, so the expected range has not changed. The search continues!

Day vs. Night

Crazily enough, this was almost exactly the same as indoor vs. outdoor.

A one point difference!

Obviously, this speaks for itself after indoor vs. outdoor.

Over/Under

This strategy was told to me by another user, so I decided to look into it. Basically, the idea was that a game with a higher over/under in Vegas meant that more points would be scored, and, hence, more kicker points. This one seemed to have the most merit, but the results were much in the same. I averaged out the over/unders for the entire season, and came up with 45.2 and used that as the basis for my analysis.

The total games for this was under the amount for the others due to not being able to obtain the over/under for some games. Regardless, 480 is a good sample size, and shows the same thing we have seen in the other 3 examples. Standard deviation has not changed much here either.

Obligatory kicker picture to break up the monotony. The Lions sure have pretty blue jerseys!

Defensive Match-ups

Now this is where I thought that I would have to eat crow and say there is a strategy to kickers. A couple of theories were suggested, such as a defense that was good enough to stop touchdowns, but bad enough to give up yards and thus let teams get into field goal position. I was hoping to find out that this might be the case, and that kickers may actually prove to be less random than I had so vehemently argued they were. First, I broke up the scoring into 3 groups: versus top 10 defenses, versus bottom 10 defenses, and then versus everyone else. For this, I used total yards against for the defensive ranking based on the previous theory.

A correlation! Perhaps…..

Success! Maybe. Over a half a point average is somewhat significant, and in this case kind of expected. Poorer defenses should give up more points logically. The standard deviations were still fairly high, about 3.5 for both the top 10 and bottom 10 groups, and around 4 for the middle. That made the expected range of outcomes for top 10 3–10 points, middle group 3.5–10.5 points, and bottom 10 4–11 points. Obviously if you are playing match-ups, you would not play your kicker against a top 10 team, and teams close to the top 10 would be kind of risky as well. The bottom 10, however, is always an enticing match-up. Can that be reliable though? I decided to dig deeper by looking at all 32 defenses. I wanted to see if you had chosen a kicker against a particular defense if that would have gotten you a better points per game average than the best kicker for the season. Season split after game 9.

I spent way too much time on this.

The correlation I was hoping for pretty much vanished. Yes, the Giants were worst in yards against, and also gave up the most kicker points for the season, but other bottom 10 teams were either really good against kickers (Miami), or middle of the pack. Also, looking at the first and second half splits, Tennessee was really giving up kicker points in the second half, yet the first half they gave up the least. Baltimore, 8th ranked defense, was just about the opposite. The standard deviation again tells a story. Denver averaged 6.38 kicker points against, but with a standard deviation of 4.01, the range would be between 2 and 10 points. Miami gave about the same amount of kicker points, 6.56, but its standard deviation was 2.71, giving an expected range of 3.85 to just over 9. So Miami, being a bottom 10 defense, was arguably as good or better than the number 1 defense at keeping kickers at bay. That makes it difficult to predict match-ups with defenses.

The lines are hot lava! Jump over them!

So just draft a top kicker you say. Well, yeah, that will generally work. However the weekly result is still not all that predictable or consistent. Let’s look at the top 5 kickers in total points. I used top 12 as the cut off for start-able, as most leagues are either 10 or 12.

What I look for most in a player is how often I can play the player with confidence that they would be better than any scrub off the waiver wire. An argument could easily be made that Gostkowski and Gano are every week starters regardless, and I can respect that. However, there are still 4 weeks when just about any kicker will score as much, or more than them. Also, the expected range of scoring is so large still. The average #12 kicker scored 8 NFL points over the course of the season. Even the best kickers’ expected range of outcomes, determined using the standard deviation, starts well below that at around 5.6 points or less. To me, that is not comforting.

Some of this data obviously is open to some interpretation as to whether or not it is useful for kicker strategy, especially the individual team data. To me, this seems to cement my distaste for kickers in that there is no strategy in picking good performers. That is my opinion, and my only hope is that the data I have presented to you has been useful in forming your own. Thanks for reading!