The Fundamental Thinking of Single-Entry Play
“JM, I have a question for you.”
“I’m listening.”
“Okay. Let me see if I can get this out so that it makes sense…”
“Take your time. I’m in no rush.”
“It’s just…you talked in the last lesson about some of the reasons these guys take the mass-entry approach, and you touched a bit on the skills involved in that approach. So I guess my question is: If it’s possible to study, learn, and develop those skills, why don’t you take that mass-entry approach yourself? Or…more accurately: Why should I stay away from the mass-entry approach?”
The first thing I need to reiterate here is that different approaches work for different DFS players. Just because I stick to single-entry or limited-entry play does not mean this is the approach that will work best for you.
As I also said before, however: The knowledge necessary to succeed with a multi-entry DFS approach or a mass-entry DFS approach builds on the knowledge necessary to succeed with a limited-entry or single-entry DFS approach. For that reason, it is extremely valuable to take the time to learn the “why” and the “how” of the single-entry approach, even if you eventually come to discover that “more entries” works better for you. What’s more, I am convinced that the massive majority of DFSers would perform much better if they tightened up the number of entries they chose to put into play.
I’m going to dig a bit deeper into that thought in a bit, but first I want to explain what I mean in the separation between “single-entry,” “limited-entry,” “multi-entry,” and “mass-entry.” Looking at the distinguishing features of each category will help us to wrap back around to the reasons I feel most DFSers would significantly bump up their profits if they pulled back on the number of entries they played in any given tournament.
This is an extremely important lesson in this course. It will also probably be the longest lesson in this course, so grab a cup of coffee if you need to. Get comfortable. And let’s go!
Mass-Entry DFS Play
Mass-entry DFSers are those who enter a massive number of entries at once (duh). Depending on the size of the tournament, “a massive number of entries” may be anywhere from a couple dozen entries to a couple hundred entries.
Mass-entry play should not be confused with someone who enters hundreds of entries into a double-up or 50/50. Typically, top DFS players will max out the allowable number of entries in a large-field double-up or 50/50 in order to get the most out of their optimal roster. Typically, these individuals will use the same roster on every one of their entries (or, if they divide up their entries among a few different rosters, they will do so with a small number of rosters in play). “Mass-entry,” instead, refers to tournament players who put a large number of unique rosters into a tournament.
Different mass-entry DFSers have different approaches from one another, of course, but typically, a mass-entry DFSer will determine the expected ownership percentage of various athletes, and will then decide how they want to leverage their own ownership as a result.
A great illustration of how this works can actually be taken from a non-DFS situation: The March Madness tournament.
I am wrapping up the creation of this course in March, 2016, and for the first time ever this year, I applied “mass-entry game theory” to my NCAA Tourney brackets. I didn’t have the idea of doing this until it was too late for me to actually enter a massive number of brackets across a variety of pools, and I didn’t have time to actually crunch all the numbers as fully as I would have liked, but I did have time to think through how this would work, and to apply a basic outline of this approach to the 15 brackets I had in play. Follow along here for a moment; as I said above, this example actually provides an easier path to illustrating the true thinking behind mass-entry DFS play than any DFS-specific example could.
I don’t watch college basketball (or follow it at all) until mid-March, but I love March Madness. This is a conundrum that leads to a lot of fun during the NCAA Tournament, but to zero profitable years in March Madness bracket pools. This year, however, I realized that instead of reading a bunch of articles the day before the tourney begins and trying to make my own picks, with a single optimal bracket, I could use the advanced information available to build a game-theory-driven set of brackets.
Firstly, I accessed the interactive bracket on fivethirtyeight.com, where they showed the percentage likelihood of each and every team in the tourney advancing to each and every round. Because fivethirtyeight.com is known for their advanced statistical approach, I figured this would give me as clear a picture as anything could of how likely each team was to move deep into the tournament.
Secondly, I looked at my CBS bracket to see the percentage at which users across the country had selected various teams.
Thirdly, I applied mass-entry game theory to my brackets using this information.
A great example was Michigan State. If you reach back in your memory to the 2016 NCAA Tourney, you’ll recall that Michigan State was a two seed, but was a very popular pick to win the tourney. In fact, they were the second most popular pick in the bracket. But while fivethirtyeight had Michigan State projected at an admittedly very high 32% chance of reaching the Final Four, a full 64% of users on CBS were picking Michigan State to reach the Final Four(!). Although fivethirtyeight’s predictive metrics obviously thought Michigan State had a better chance than anyone on their side of the bracket to reach the Final Four, this team was nevertheless being severely overvalued by those picking brackets (32% chance of reaching the Final Four; picked on 64% of brackets).
If I were building 100 brackets in this situation, and all things were equal, I would want to pick Michigan State in 32 of my brackets (matching the 32% at which fivethirtyeight projected them). Because twice as many people were picking Michigan State as should have been picking them, however, mass-entry game theory would tell me that I should actually cut my own exposure to Michigan State down to half of their expected chance of reaching the Final Four: in other words, because twice as many people were picking Michigan State as should have been picking them (so that Michigan State was in the Final Four on 64 out of every 100 brackets), I would want them in only 16 of my 100 brackets (half of the number that should have been picking them) in order to go light where others were going heavy. As a result of going light on a team where the field was heavy, I could also then go heavy on a few teams on which the field was light. If Michigan State then got knocked out of the tournament early (as they did – getting upset in the first round and severely impairing 64% of brackets at once), I would gain a massive edge on the rest of the field. And on the brackets where I did pick Michigan State, I could balance out my “chalk” in that area by pairing that Michigan State pick with some contrarian selections elsewhere in my bracket.
Now, that’s a bit of a simplistic illustration, for a few reasons. 1) A March Madness bracket has a binary outcome: the team you pick wins or loses (and the team others pick wins or loses). In DFS, there is a wide range of outcomes each player can have. 2) In March Madness, there are lots of places where you can find the rate at which “the field” – across all brackets entered – is picking each team, in each round. In DFS, you have to use your knowledge to make credible guesses on ownership percentages (more on this in Lessons 6 and 7!). 3) Because I don’t follow college basketball, my decision of whether to go light or heavy on a team had to do solely with how light or heavy the field was compared to how light or heavy they should have been according to fivethirtyeight. In DFS, however, one has to determine on their own how light or heavy they think the field will be on a player, and how light or heavy they want to be as a result – whether they want to undercut ownership, go even heavier than the field on ownership, or stand pat with the field on ownership.
In spite of the flaws in that illustration, however, I feel it paints a great picture of the fundamental thinking behind a mass-entry approach. In a (successful) mass-entry approach, a DFSer essentially says, “What percentage do I expect this player to be owned? Where do I want to be on ownership as a result?” If I’m a mass-entry DFSer building 100 teams and I project a player at 40% ownership and want to be at only 20% myself, I’ll be a bit more contrarian in my other spots on the 20 teams on which I use that player. I’ll also have 80 teams without that player that can gain an edge on a large chunk of the field if that player has a disappointing game.
Multi-Entry DFS Play
Multi-entry play is the label I put on a strategy that entails a more manageable number of entries. This still might be three or four entries in a 100-entry tourney, or seven or eight entries in a 400-entry tourney, or as many as 40 or 50 entries in a mega-sized tourney (such as the Millionaire Maker on DraftKings – which ranges from 300,000 to 600,000 entrants). But generally, this approach is less about “gaining exposure to a massive number of players and simply deciding where to be overweight and where to be underweight,” and is more about hedging ownership in strategic ways or building lots of entries around a specific core. This approach is an amalgam of “mass-entry” and “limited-entry” play, so let’s jump down to “limited-entry” to understand this better.
Limited-Entry DFS Play
Limited-entry play is very similar to single-entry play, in that a limited-entry DFSer will generally build only the number of teams they feel is necessary to build based on their research. The strategies on how to manage these entries vary. Most limited-entry DFSers will build around a core of players – rotating a few different ancillary pieces around a few players that are on every team – though I have also known limited-entry players who use an ownership-hedging strategy not unlike the strategy used by mass-entry DFSers…though, of course, doing this while managing only a few entries (that is: doing this successfully while managing only a few entries) takes its own special skill, to ensure the DFSer in question is actually doing more than simply “throwing away entries by spreading ownership widely across a small number of rosters.” With the successful application of this approach to limited-entry play, a DFSer will usually be near the top of the tournament field with one or two teams, while being near the bottom with another two or three entries. This is simply the way things go when each team across a limited number of entries looks entirely different – though, of course, this is a profitable approach for the small number who can master it.
Single-Entry DFS Play
One single entry. Every single time.
Now, I’ll be honest with you: For a while, I fell into the trap of becoming married to my identity as a “single-entry DFSer.” I cannot tell you how many times I considered putting in a second entry (or a second and third entry), with a slight variation on my “main team,” only to say, No, JM, don’t do that – you’re a single-entry DFSer!
I talk quite a bit about being a single-entry DFSer, and part of the reason I talk about that is because I think it’s valuable to illustrate that one can be profitable as a single-entry DFSer, dispelling the myth that “only those with enough bankroll to buy up a ton of entries can win money.” But at the same time, I have had to realize that there are times when it makes sense to build a second or third entry.
If you choose to attack DFS as a single-entry DFSer, you should approach each slate aiming to build only one entry. But if you run into a day on which you need to expand to two or three rosters, go ahead and expand!
The Fundamental Thinking of Single-Entry Play
Here is the fundamental thinking, for me, behind the single-entry DFS approach:
If we can narrow down a pool of players to the most +EV plays, why not do it?
That’s it. It’s as simple as that.
If we can narrow down a pool of players to the most +EV plays, why not do it(!)?
Over the next two lessons, we are going to look more deeply into how, exactly, we can go about narrowing down a pool of players to the most +EV plays (+EV = “positive expected value” – in other words: the most +EV play will be the player who has the highest likelihood of bringing you a big return on your investment). But first, we need to talk about what +EV actually means.
One of the great misconceptions of DFS is that anyone with in-depth sports knowledge can succeed. The reality is that knowing a sport really well is only half the battle. The other half is understanding DFS really well – knowing the strategies, the game theory, and the approaches that can help you gain an edge on your direct competition: the other DFSers who have entered the tournament you have entered!
If a particular pitcher is going to be around 60% owned, there’s a pretty good chance this guy is a strong play. Right? – otherwise, 60% of the field would not be rostering him. But even if this guy has the highest likelihood, out of all pitchers pitching that day, for a big game, this does not necessarily mean this guy is the most +EV play.
Just because an athlete has the highest likelihood at his position for a big game that day does not necessarily mean that this athlete is the most +EV play!
In the example above of the 60%-owned pitcher, what if there is another pitcher in the same price range whose point expectation is only a couple points lower than the pitcher who will be high-owned? And what if this “slightly lower-projected pitcher” will be owned at around an 8% clip?
If you roster the 60%-owned pitcher and he has a big game, that’s great…but you need to finish in the top 20% of the field just to cash in most tourneys, and you are ultimately aiming to take first place in any tourney you enter. Even if you pick up a big game from that 60%-owned pitcher, you still have a ton of work to do with the rest of your roster in order to reach the top.
But here’s where things get fun: if you fade that 60%-owned pitcher and he has a poor game, and your 8%-owned pitcher posts a big game, you have now soared past a huge chunk of the field, and are just about guaranteed to cash in tourneys, while competing for first place against a much smaller number of entries!
This is where the intersection of “sports knowledge” and “DFS knowledge” happens – where you begin to be able to identify the most +EV plays on the day with an understanding of each of the two important elements that are necessary for sustained DFS success.
Of course, before you can pull this off, you have to have that sports knowledge and that DFS knowledge.
Oh, look at that! The next lesson is titled *Know the Sport*…