If you’re a Fanduel player who puts in any sort of volume, you have probably had frustrating lineups that missed higher bracket GPP payouts because your kicker blanked on you. You’ve probably also had lineups where a WR bricked on you, or a RB got hurt the first series and put up a goose egg, but those are less memorable and more forgivable than a kicker scoring in the low single digits. I’ve known several players who only play minimally on Fanduel and choose other sites to play most of their bankroll on due mainly to having to roster a kicker. I’ve also known many players who plug in the cheapest kickers because it’s a “crapshoot.” There are even sites that give daily fantasy advice that advocate blindly picking the “best worst cheapest” kicker, or simply don’t list kickers in their suggestions because everybody knows kickers are way too random.

When I first started playing on Fanduel, this was my approach. After having early mild success, I went on a big 2 week downswing primarily due to my cash game rosters having kickers that scored 3 and 0 the first week, and 3 and 1 the second week. There were spots where I could’ve optimized more with better research, but at the time all I could think was I’ll never win on Fanduel because I’m not good enough to make a roster to fade having the randomness of picking a kicker every week. After withdrawing all my Fanduel money out of frustration and swearing off kickers for good, a friend of mine who is a high volume DFS player reminded me that kickers require research time just like every other position. Simple advice, right?

A kicker’s absolute floor is obviously 0, while his absolute ceiling appears to be 29 (Rob Bironas in week 7 of 2007 went 8/8 for FG with a 40 and a 50 yarder and 2/2 on XP’s). I like to adjust the floors to 2 and the ceilings to 24 for a more likely number. I also want to hold myself accountable for my picks, so I set aggressive floors and aggressive ceilings for kickers I view as favorable. When I first started doing all this kicker research, the ultimate goal was to avoid having a kicker brick on me. It is highly unrealistic for me to accurately project ranges of points, but not unrealistic to project opportunities. While kicker’s scoring can be random, the same is true for every player on every team for fantasy. Did you know last season Peyton Manning scored 8.5 points in a game, Andrew Luck scored 5.5 before being pulled in the 3rd in a blowout (granted this was late in the season when the Colts had nothing to play for, but he did play for 3 quarters), Tony Romo scored 9.9 against the Eagles at home, Lynch had 4 games under 9 points, Forte had 5, Lacy had 4 games under 6. The point being is if I told you any of these superstars were going to score these amounts, even against the best defenses, you would be skeptical of my advice. The same can be said about kickers.

There are some kickers who have a high average, and on a week to week basis they will often score way above that average. On the weeks they don’t, that average is brought way down. The kickers who have a low average rarely score much more than that number, and their average becomes inflated the rare weeks they go off. Where FDPP$ comes into play is all evident just by looking at the weekly scoring chart for 2014 posted below. The higher average scoring kickers are going to be in the $4,900+ bracket. Those kickers are averaging higher than most of what the lower scoring kickers’ ceilings are. This is not to say that every week a $4,900+ player is the correct play, nor is it to say $4,600 and under kickers are always the wrong play.

It’s saying that unless you feel confident in your ability to field a lineup that can consistently carry you through having a kicker that will on average yield you at best 6 points, then you need to start at least considering higher priced, more consistent options. Add to the fact that to hit a big score GPP, you have to essentially run perfect, you end up rostering a kicker that will be heavily owned making it harder to move up the pay scale that statistically has little chance of being one of the highest scoring kickers of the week. I feel much better on Sunday’s knowing that I don’t have to worry about my kicker that week. Finding $$$ in your lineup to spend up at kicker is easier than it would seem, something I hope to break down further in the future.

There will be anomalies in the data, like there are with any data. In Week 15 last year, 3 of the top 6 scoring kickers were in the $4600 and under bracket. Josh Scobee was $4600, had his highest scoring week of the year with 18. His next highest total for the year was 10, and he averaged an inflated 6 for the season due to this game. That same week, Ryan Succop scored 12, the most points he had scored since Week 1! For the year, Succop only had 4 games where he scored above his average, which was 6. Now let’s take a kicker like New England’s Gostkowski, a kicker who was regularly priced between $5100­ $5400 during the season. He was the highest producing kicker for the entire season, but his highest point total was only 15. However, with an average around 10, he only scored under that average during the season 4 times. This is a sharp contrast. I’m not recommending that you only roster the highest price kickers, I’m only suggesting that punting kicker every week is the most common tactic for the public, and there’s a reason they are the public. You are spending $4500 in hopes that he will go off against all statistical data that week. When you spend $4900­$5000 and more, typically that kicker is averaging what the $4500 kicker will “go off” for.

I aim to score 8­10 points with my kickers. If I score 8­10, that relieves the frustration of having a losing lineup due to bricking off at kicker. Perhaps I am trying too hard, maybe I’m looking for stats that aren’t there. All I know is the lineups I created after I started working on my kicker research were generating more points than before. This is probably due to the amount of research I was inherently doing on every team because of trying to find good spots for the kickers, but regardless, I came to the conclusion that 8­10 points is what I’m searching for.

Armed with a new life goal and a blank slate, I delved into the same offensive and defensive stats I had always looked at with new purpose. I’m not a mathematician nor a brilliant programmer, so I began by hand picking different stats and trying to build a correlation between them. As with any research, there are stats that you can use to prove anything is true, but as I kept comparing different stats, I found the numbers becoming fairly linear and consistent.

The very first thing I did was look at the past 3 seasons to find which teams kickers scored the highest points to see if there was any consistency. Standard scoring is used in these charts for research purposes.

kicker score consitancy chart

It should be noted that the teams in red all had special circumstances; 2012 was Andrew Luck and Russel Wilson’s rookie season, and Packers kicker Mason Crosby shanked his way to 21 for 33 (63.6%) on Field Goals. If you were to bump those numbers up to be on par with what those teams produced the past 2 years, there are very few anomalies in the consistency year after year. This graph is the backbone that allowed me to expand on my kicker research. If you were only this information to base your picks on, you could quickly come up with a few conclusions:

  1. Kickers on teams with elite quarterbacks score alot of points
  2. Kickers on efficient offenses score alot of points
  3. Kickers on teams that win alot of games score alot of points.

This information seems so obvious, it’s painful for me to even make you read it. However, it’s important for you to understand that in an overall sense, there is no magic formula to picking kickers. The better kickers are going to cost more on Fanduel, but they’re also going to yield more points per game on average.

Let’s expand on this information by looking at 2013 and 2014 season rankings for some more statistics. The green boxes indicate top half of the league in that statistic, red boxes are for bottom half.

2013

2013 season ranking information

2014

2014 season ranking information

Alright, now we’re getting somewhere. While not an exact science, these statistics can start to lead us in the right direction for finding point opportunities for our kickers. These are season long stats, so how are these supposed to help you know which kickers are going to perform week to week so you can score all the points? By trying to find correlations, of course.

  1. The first correlation I like to point out when looking at these charts is the Offense PPG (Points per Game) Rank. 5 of 32 qualified teams in the top half of the league in Offensive PPG weren’t in the top half of the league in kicker scoring. 6 out of 32 qualified teams in the bottom half of the league in Offensive PPG both years finished in the top half of the league in scoring. Therefore, there is a strong case for a correlation between points scored and kicker scoring.
  2. If you look at the 3rd Down Conversion % Rank, you will notice in 2014, there was a much stronger correlation between that and 2013’s numbers. This statistic is more varied over season long breakdowns, but provides us with invaluable insight during the season. When we move into assessing weekly stats for more accurate projections, this statistic become much more important, but we will discuss that later.
  3. The total plays number is something that became very important at a later stage of my research development. Pace = Opportunity = Points. The more plays a team runs, the more chances the offense has to put itself into scoring position, which leads to more chances to score points. You will notice at first glance that, no, amount of plays run does not directly correlate to amount of points a kicker scores. However, if you look at the team and consider their offense, you realize most of those teams have inefficient offenses. Plays run per game is an opportunity to score points. For weekly purposes, when you see a middle of the road offense playing against a bottom tier defense, this is just another tool you can add to your kit.

Let’s start bringing this information together and see how we can apply it to Fanduel in order to pick kickers that are going to provide us the best opportunity to score points!

A daily fantasy player much better than I am once told me that he aims to get 30 points from his kicker and defense combined. I feel like that’s an accurate and achievable number. An uninformed decision at any position can make or break your lineup, even the kicker. Each week, you should be trying to build a lineup where your players are expected to be in a good position to provide you the most amount of points per dollar spent. I don’t believe in the art of projecting the number of points a player will score, but I do believe in using statistics to project a range of scoring, very similar to the art of assigning an opponent a range of hands in poker.

Punting at kicker has become a very popular go to strategy for lineup construction due to the volatility at kicker. The following chart shows the scoring of all kickers on a weekly basis, not including the kickers who didn’t play full seasons. This chart uses Fanduel scoring, to get an accurate representation of the data presented.

weekly kicker fantasy sports information

The first thing to notice is the consistency of the kickers scoring week to week. The second thing I like to have people look at is each kicker’s highest scoring game relative to his average. Ryan Succop from Tennessee was often a minimum priced ($4500) kicker last year. His season long average points per game was 5.8. Most of his weekly scoring was around this number, save week 1 when he scored 16 points, which has almost a full 1 point difference on his average. Josh Scobee from Jacksonville was also frequently a minimum priced kicker, averaging 6 points a game. He also had one very high scoring week 16, in which he got 18 which inflates his average. Now take Gostkowski, often one of the more expensive kickers on weekly slates (~$5200). His week to week scoring was more varied than some of the lower priced kickers, but because he scores more points on average than the bottom kickers, low scoring weeks have a much bigger impact on his average. Knowing a kickers floor and ceiling are important when making salary considerations. Let’s look at a chart from Week 13 which shows Fanduel price and points scored.

fanduel kicker performace

My predictions for this week were that 2 of the top 4 kickers were going to be from the 4900 bracket. I had Bryant, Carpenter, Crosby, Walsh, and Novak, all priced at $4900, listed as likes for the week. All the games either had higher over/unders, or had offenses that were in form against opposing defenses that weren’t. Only one of the 5 I had listed was in the Top 4, but 4 of the 5 were in the Top 8. I listed the aggressive floor at 6 points, which all hit, and the aggressive ceiling at 14­18, which went 3 for 5. My bankroll lock of the week was Mason Crosby. The Packers were 3 point favorites at home in a game with a total of 58.5​. That total was extremely high, which had a huge impact on that prediction.

At face value, this chart shows us very little. It shows us that most of the kickers that scored 10+ points cost $4900 or more. It also shows us that the lowest priced kickers didn’t score the least amount of points, while the highest priced kickers both scored less than the lowest priced kicker. However, if you were to delve a little more into these statistics, you would notice the following: the Colts, an elite quarterback played home against the Redskins, who had a bottom tier pass defense give up 5 TD’s to Andrew Luck. The Jaguars and their mediocre offense played AT the Giants, who had a bottom tier total defense. Also of note, the average score for kickers in the $4800 to $4500 bracket was 5.5. The average score for the $5000 to $4900 bracket was 11.6.

Over time, I started to import more statistics into my charts to give me more variables to factor in an effort to more accurately predict ranges. If you follow football meticulously throughout the season, you will remember which teams are scoring the most PPG, which have the best and worst offenses, where a team’s weaknesses on defense are, etc. You can begin to utilize the first charts I showed you into your other research when trying to determine whether a kicker will have more/less opportunities in a given week.

All of the stats shown to you so far are great, but none of them matter if you don’t factor in Fanduel salary, right? Great, more expensive kickers yield more points on average, but what if you simply can’t fit a +$5,000 kicker into your roster? Well, you don’t have to every week. More often than not, the higher priced a kicker is on Fanduel directly correlates to how good that team is versus how good their opponent is. Vinatieri will be more expensive against Jacksonville than he will be versus the Dolphins than he will be versus the Seahawks.

When crafting a roster, whether you know it or not, you’re trying to create a team that provides the most points per dollar spent, or on my charts read as FDPP$(FanDuel Points Per Dollar spent). The following chart is an expanded look at Week 14. The % Owned statistic is taken from the Sunday Million, which gives us the biggest field to draw a percentage from.

week four info

This chart provides us with much more information, allowing us to draw a few more conclusions. The most important to me % Owned. See Graham at 34.2%? This was the guy everybody was recommending as the punt play of the century in their articles. There was no other analysis that week. The Saints were ­9.5 at home against the Panthers with a total of 49.5. The Saints played at the Panthers 4 weeks earlier, and the game only had a total of 38, with the Saints winning 28­10. The Saints were 4­4 going into that game. They followed that with 3 straight losing weeks AT HOME. Going into the second matchup, the Saints were 5­7 and had been struggling at home with the offense lacking any power (remember those weeks Jimmy Graham was blanking?). What ended up happening that week was the Saints lost 41­10. So far, we haven’t included that many stats in the charts, so it’s hard to really see where the projections come from. I’m attempting to show you the evolution of the process, and how with statistics you can try to predict ranges. Now, I’ll show you what I use to bring the entire thing together.

  1. Red Zone Scoring Attempts per game (last 3 games) combined with Red Zone Scoring Percentage – TD’s Only (last 3 games)​This gives me a good idea of how many times an offense is getting field position in order to kick a field goal, but how often they fail to score a touchdown, thus often resulting in a field goal. The higher the attempts and the lower the TD scoring percentage is an accurate assessment of a team’s offense, as if the RZ Attempts is high, it generally means the offense will move the ball to at least Field Goal range (~35 yards).
  2. Over/Under and the Line ​I look for away teams that are favored to win first, regardless of the spread or the total. My theory is that away teams are happier to take the points when they can, as it is typically harder to score than at home. If a kicker I like is at home, that team has to be the favorite, and preferably by quite a large margin. Kickers on teams that are dogs are subject to 2 negative impacts: 1) If they’re behind, chances are the offense will be more aggressive which leads to more turnovers and more going for it on 4th down 2) Opposing offenses are going to attempt to grind the clock out, providing less opportunities.
  3. Kickers team 3rd down conversion rate​While you would prefer a team with an extremely low conversion rate, you don’t want to run the risk of a team that simply struggles to convert on 3rd downs, as this won’t allow the offense to put the kicker in a position to get a field goal off.
  4. Kickers opposing defense’s Opponent’s 3rd down conversion rate I​just like seeing how these 2 stats mesh together.
  5. Weather ​The warmer the game, the better. Rain is a negative. Wind, contrary to popular belief, is a very minimal negative. It takes winds of +15 mph to alter a football’s path in the air when kicking a field goal. Games in domes are favorable. Games on artificial turf are more favorable than those on grass. Games in Denver are also favorable. This is a cool study that was done assigning values to different factors for every field goal kicked from 2000­2011, for any of those interested in these nerd stats. 6) Fantasy Points Against T​his can be a very ambiguous number, but it can translate very well when used in conjunction with your other stats. If a team defense is allowing many points against, generally it means the defense is giving up enough yardage to allow other teams to get into scoring range.

Now that you know what factors go into the analysis, I will show you the application of it. The following are excerpts from my article I wrote for Week 14.

“For this week, Phil Dawson is my bankroll lock of the week. This is a great price point ($4800), so let’s examine the criteria which makes me salivate at having this kicker rostered this week. First and foremost, San Francisco is playing AT Oakland and are 8 points favorites in a game with an O/U set at 41. These numbers alone are enough to warrant rostering him, but let’s dive deeper. Oakland is giving up the 4th most points to kickers so far this season, at a little over 9 per game. San Francisco is only averaging 2.3 Red Zone attempts per game in their last 3, that was against Seattle, Washington (good run defense), and the Giants. Oakland, however, is allowing a little over 3 Red Zone attempts per game in their last 3 games, against the Rams, Chiefs, and Chargers. While at face value these numbers don’t mesh with our formula, you have to consider the big picture. San Francisco is only converting on 40% of third downs, while Oakland is FIRST in the league at stopping opponents on 3rd down in their last 3, keeping opponents to an 19% conversion rate!!! This is due to Oaklands turnover differential, which is a league worst ­18. Oakland is turning the ball over inside their own half of the field, teams are running the ball 3 times, then kicking a field goal. I am expecting Dawson to get an optimistic 5 opportunities to kick a field goal Sunday, and see his aggressive floor at 10 and his ceiling at 20.”

Dawson ended up only scoring 9 points this week, going 2 for 3. His miss was a 47 yarder, and he had a 54 yarder that was good called because of holding, which ended up forcing them to punt. While impossible to know how the game would’ve ended up, Dawson scores us AT LEAST 14 if that field goal wasn’t called back. You can see the entire process broken down here. If you are conscious of season long stats, you can use the last 3 games to make better judgment calls. If a team is a really good defense, but their last 3 games show their defense hasn’t been so good, you have to look at their last 3 opponents and interpret the data accordingly. Here is a look at my variation for that week.

“Where I’m not playing Dawson, I’m going to attempt to play Crosby. Green Bay are 12 1/2 point favorites at home in a game that is supposed to be in the teens. As of right now, the weather is supposed to only bring flurries, therefore a massive amount of snow is not likely. Green Bay is one of the best at converting on 3rd down, while Atlanta is 2nd in the league over their last 3 at stopping opponents on 3rd down. Green Bay is averaging 4 Red Zone attempts per game, converting to a touchdown 45% of the time. This isn’t exactly favorable in the formula, but this has all the potential in the world of being a 4th quarter offense led by backup Matt Flynn, led by Eddie Lacy. I feel like Crosby is around the same scoring range as Dawson, with a floor of 8 and a ceiling of 17.”

Crosby ended up scoring us 15, and the process worked itself in our favor. The kickers I predicted to pay for themselves were Hauschka, Brown, Bryant, Sturgis, Carpenter, and Zuerlein. I predicted all of these kickers to have a floor of 6, and all of them eclipsed that except Zuerlein (4). The kickers I was against were Graham, Scobee, Catanzaro, and Janikowski. Catanzaro scored 10 and Janikowski scored 8, which was against our model.

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