I got a lot of requests to analyze kicker streaming. So I crunched a whole bunch new simulation programs, to try and find out whether it's better to hold or stream kickers. There were some mixed inputs from discussions here. I thought it was obvious that kickers could be streamed, but some responded that it's better to hold a top kicker. (As you'll see… the discussion is really about "what do we really mean by top kicker".)
Streaming can be at least as viable as holding (on average) depending on what your league-mates do and the quality of the kicker ranking source.
Being able to say you held the top kicker is mostly hindsight and luck.
My (updated) kicker projection model should help you get top-kicker numbers by streaming.
Just like my older post about streaming QBs, in the below I will examine plots of average kicker scores you get depending on how many kickers are already taken (so you can interpret the results depending the size of your league). Please see more details in my QB post. These kicker results are based on the 3 years of data I had at the time.
This section will show how having a top kicker is usually known only in hindsight. Therefore, it's a serious question to ask how you go about getting one. You'll see.
Firstly, the orange-ish curve below shows the average points scored by a #N kicker, looking back on the season AFTER all outcomes are known. So the curve represents having complete perfect knowledge of holding each N-ranked kicker from the start of each season. Note: I am employing a 3-running average on all these plots, because I think it better conveys the results and is easier on the eye.
The first point I want to make is that this "post-season" assessment of #N is different from "in-season" experience. Meaning: You can have the #1 kicker at week 6 (say, Slye in 2019), but that same kicker later is no longer the #1. In week 15 of 2018, Fairbairn just overtook Lutz for the #1 spot-- and who knows maybe Zuerlein would have overtaken both of them if there were more games in a season. Therefore, to better reflect "what was it like to own a #N kicker IN season", I instead show a weighted average of season scores, in the same way as I described in the QB post. This is the blue curve, and I am using it to represent a target that we can reasonably strive for (whereas a target 10pt avg is not really reasonable).
Although this represents "how it was to own a #N kicker", the important point I want to make is: the blue curve is still in hindsight. This is because it does not tell you how to pick those kickers before they build up their in-season scores. To illustrate, let's consider the strategy of streaming top-scoring kickers. This means, for each week, you simply choose the kicker who has scored the most so far. By definition this strategy gives you a "top kicker"...-- so shouldn't this be ideal for ensuring that you get the "top kicker experience"? After all, if it's favorable to "hold your top kicker", then logically this strategy should surpass the scenario of plain holding. Well, here's what it looks like if you could stream that way:
This orange curve represents the truer result of basing your strategy only "who is a top-scorer". The result: you would underperform, relative to the ideal line of in-season hindsight. The reason is actually simple: the ideal-hindsight curve includes all the scores made by the kicker in earlier weeks-- it assumes that you used those kickers even though you didn't know yet they would be on top. However, in this theoretical scenario of streaming top scorers, you don't actually get any benefit from those earlier high scores-- because you haven't been holding them the whole time-- you missed out on positive randomness. Therefore this Top-scorer curve makes a great depiction of "regression" as people normally talk about in fantasy.
You might also think "But maybe I can know who the top kicker is likely to be, before the draft", and well... we can try to look at that too. Here is how kickers did according to one source of recommended draft positions. (I chose the best-performing of 3 draft rankings I could dig up, and I artificially downgraded the outlook for 2018 Zuerlein due to the early injury, to try to give you a fair picture):
The top 3 picks are often decent enough..., but otherwise there is still a 1-point gap from the ideal-hindsight line.
So: if you can't reliably pick the most elite kicker at draft, and if you also can't reliably get a top-kicker experience by streaming the top-scoring kickers, then how can you realistically achieve the experience of owning the #1 kicker? I think the rest can only be luck. But let's look at just one more idea. Maybe the top kicker is determined at the halfway point of the season, so you should hold kickers according to their rank at that point? Here is the outcome of that...
Eerrrgghhh..., it's closer... in a way..., right? But unfortunately, the top 3 "elite" kickers appear to burn out a little bit after the second half of the season. Their 8 point average in the back end isn't awful, but it clearly does not meet the ideal-hindsight line, and I will show that it should be easy to beat by streaming. If you over-interpret , you would say the best strategy is to aim for the #5 kicker at mid-season.
TLDR of this section: It's of course best to own top kickers in hindsight; however, even near the end of the season, it can be difficult to know who they will be. Choosing your kicker based on prior score is a decent strategy, but it cannot meet the expectations you probably have for owning an "elite" kicker.
Now I'll show the fantasy points you'd get from streaming, according to somewhat "ordinary" kicker projections. For now, I am not implementing my own, full- u/subvertadown model, but instead a more rudimentary model. But with an accuracy correlation coefficient of 0.23, I believe this simple model surpasses many/most kicker ranking sources I've seen. So if you stream according to these basic matchup projections, then the average score curve looks like this:
This strategy, of streaming by matchup, seems at least as viable as taking the kicker who has scored the highest. If you can get one of the top-5 streaming options in a given week, you will probably marginally beat the "top-scorer" expectations. I'll show in the next section how it looks even better when you can get those top 5.
There's an important point to clarify, which is that all the above curves assume that your league-mates have the same kicker selection strategy: each value at position "N" assumes that all the "N-1" are taken. So the #10 position for matchup-streaming assumes that your league-mates are also streaming the same way and they have already picked (on average) the 9 best matchup-streamers. Likewise, the #10 position for Top-scorer streaming assumes your league-mates have already chosen the current top-9 scorers.
Here's an example of why this matters with kickers so much (whereas it did not in my QB analysis). Imagine that your league-mates just always go for the kicker with highest season average, every week, and you would in principle be left with a low pick, e.g. #12. But instead of you settling for #12, you are the one guy in your league streaming, by the matchup model above. It turns out you could very often get a top-projected kicker!
We finally beat the curve!!! I guess we all want to have unaware leaguemates like this...
Now this looks great, but obviously this is not the normal situation when streaming kickers. You cannot depend on your league-mates to simply chase top-scorers, because some (most?) of them will be streaming by matchup too. So to more rigorously decide what strategy better, I have to assume what the league-mates are doing. Therefore, for the following, I assume your league-mates use mixed strategies, split almost 50-50: the #1 pick takes the best match-up projection, and the #2 picker takes the top scorer, and so on... alternatingly. Here is how the resulting curves looks, reflecting what the average outcome would be for you, if you had the Nth pick and your teammates followed this pattern.
As you can see, streaming based on matchup (by the rudimentary model) is still very effective.
In fact under these conditions, matchup-streaming is the more effective strategy on average, and it almost reaches the Hindsight curve. In any case, for early picks it is definitely higher than streaming top-scorers. Again, the reason this can work is because there will be enough good options for matchup-based streaming. If everyone's doing it, then holding a top kicker can be better.
All the above contributes to these conclusions:
You usually don't know if you're owning an elite kicker until after the fact, and at least half a season is needed to know if your kicker has that potential.
Past top-scorers do not guarantee future top scoring, and therefore matchup-based streaming can be more effective than trying to hold kickers with higher averages.
This is really all I really cared about showing you today, but... I figure some of you would like to look at the behavior of my own projection model.
I'm not showing results from the full kicker model here, rather just the out-of-season cross validation formulas. Here's how it performs against the other strategies from above, in the scenario where all leaguemates are using the same strategy as depicted by each curve.
If we believe it, my streaming model should almost be able to hit the target line.
And here is how my model performs in a scenario where your league uses the "mixed selection" strategy (half streaming, half chasing top scorers):
Why I think you should be able to expect 9-points from your kicker streaming, on average
Obviously, this only has a chance of working if everyone else is using the less-optimal strategies.
Even if you're using different kicker rankings (not mine), I think the above analysis shows that streaming kickers based on matchup can -- ON AVERAGE-- be more effective than trying to hold top scorers. But, this only works well if a projection source is accurate enough and if not all your league buddies are not doing the same thing. The experience of "having had an elite kicker" is largely based on hindsight and possibly a lucky experience at draft.
Tagged underBackground , Modeling