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Stacking is a strategy that everyone is aware of but few people understand the logic behind. Stacking is when you select multiple hitters from a single team for your lineup. This could be anywhere from two to five hitters. Although it can be a very strong strategy in general, it is a significantly better tactic on FanDuel than DraftKings. This is due to a variety of factors and by the end of this section we will have explained them all. Before we examine the differences between the two sites though, let’s analyze why stacking is a good strategy in the first place.
First, we want to clarify that each player you pick for your team stack must have the research factors in their favor. We are in no way advocating choosing players that don’t have favorable matchups just because their teammates do. When all the players in a team stack have the research factors in their favor, you are maximizing your upside without increasing your downside. If you don’t strictly pick good hitters for the stack, then your upside is obviously reduced by the bad hitters you forced into the lineup. Let’s examine why team stacking doesn’t technically increase your downside, but why our psychological biases trick us into thinking it does. We’ve all heard the old adage, “don’t put all your eggs in one basket.” This is good advice if you are building a stock portfolio for sustainable growth, or if you are drafting a season long fantasy team and you want consistent production all year. On an individual day however, putting all of your eggs in one basket is a good move. If all of your hitters on the team stack fail to get on base, they were no more likely to fail in their individual at-bats than another hitter on another team (once again assuming that all the hitters had the research factors in their favor to a similar degree). There is no reason to expect a hitter on another team to outperform the hitter who is a part of the team you are stacking just because he plays for a different team. He certainly might perform better, but it’s not because of the team stack. The most common argument people make for a team stack increasing your downside is that the pitcher you stacked against could perform well and make it hard on all of your hitters. While we agree that all MLB pitchers are professionals and are capable of having good starts, if you’re targeting a bad pitcher you’re targeting a bad pitcher–it’s that simple. Any other hitter is just as likely, if not more likely, to be in a situation where the opposing pitcher is shutting down the lineup. Diversifying yourself by picking hitters on many different teams might be comforting, but it isn’t technically reducing the chances that they all go 0-4 at the dish. Additionally, since our goal is to try and win GPPs, we shouldn’t be focused on a misconstrued idea of downside reduction. Sure, all four of them probably won’t face a pitcher that is in a groove, but we aren’t trying to spread out our risk to achieve mediocre production. Instead, we should be focusing on upside, which team stacking provides better than any other strategy in DFS.
When a player does not record an out in his at-bat, he is creating an additional at-bat for his team. The only way that could be taken away is in the event of a double play or if he is tagged out on the basepaths. This idea of creating additional at-bats is the key to the logic behind team stacking. Baseball is the only fantasy sport in which the offensive success of one player directly benefits everyone else on the team. If everyone in the lineup continuously failed to record an out with their at-bats, the team would hit forever and score an infinite number of runs. This will obviously never happen, but a lesser version of it occurs all the time, and that situation is what we are after with our team stacks. With the stack, the additional at-bats are positively affecting your lineup as much as possible. When a team is badly knocking around a particular pitcher, the person who stacked that team will likely win their DFS leagues. This is due to the difference in probability of acquiring the same amount of points with a team stack and without one. The number of individual events that must happen in a certain fashion are much greater when all of your hitters are on separate teams.
If you pick a single hitter on a team, he can only be the guy with the RBI(s), or the guy with the run on any given play, unless he hits a home run. If you have stacked a team, you could have the guy who scores the run and the guy who picked up the RBIs on the same play. To explain this using numbers, if you pick four hitters on separate teams and they don’t hit home runs, more total runs must be created to score as many points as a team stack.
To show you this, we will use the example of four hitters registering a double, one RBI, and a run. Let’s say each player hits a double in the first inning. Each of the individual hitters must have had a guy on base that they could drive in, and they must also be driven in by someone hitting after them. With the four players on unique teams, you must rely on four players to get on base before all four of your hitters. You must also rely on four other hitters behind them to drive them all in. At least two runs have to be created for that one player to register a double, an RBI, and a run. The minimum amount of runs that have to be created for the desired stat lines to occur is eight, and at least 12 batters have to reach base–barring an unusual occurrence like a series of wild pitches and sacrifice flies. If you have four hitters batting near each other in one lineup, the minimum number of runs drops to five, and the minimum number of baserunners drops to six. They can all hit doubles consecutively, which means that only two players (one before and one after) have to get hits for the stat lines to occur.
It is obviously more probable for the second situation to occur than the first, even if all things are equal. But generally if the pitcher is giving up doubles it’s a sign that he’s struggling, and therefore all things are not equal. The next batter in your stack has a better chance to succeed than research initially suggested, because the pitcher is probably now throwing from the stretch, and he is potentially rattled as he didn’t have his best stuff in the last at-bat. If he continues giving up hits, he may end up getting pulled early, and the team will be sending out a long reliever to try and get through the game.
Statistically speaking, these long relief pitchers are not as good as the average reliever, and the hitters that are a part of the stack are about to have a better matchup for the duration of the game than a team who didn’t rough up the starting pitcher.
Some people may argue that if you pick four hitters on separate teams, you have a better chance of having a player that is a part of an offensive explosion, and they are technically right, but they will still lose. Any team with a decent matchup will be stacked by multiple people in DFS, and if you are only taking one player from their stack, they will beat you if the team scores a lot of runs. The offensive explosion will serve you detrimentally if you have just one hitter from the lineup.
Our goal is to take first place in big leagues, so the individual hitter on a team scoring a lot of runs isn’t helping us because other entries in the league have gained a bigger advantage. If we never stack an offense, we can never be the beneficiary of a team batting around on a starter and facing bad relievers for the rest of the game. Our team’s success becomes dependent on many individual events that are independent of each other, rather than a few events whose probability of occurring are directly tied to each other.
In DFS, you want the result of one event to increase or decrease the probability of the next event. Mathematically, this increases your chances of ending up in first or last, which is exactly what you want. If you end up in first half the time and last half the time, you are very rich. If you end up somewhere in the middle every day (which would be anywhere from the 90th percentile and down) you have accomplished nothing, and are probably losing money. Not every night will feature an offensive explosion, and we aren’t saying you can’t win without a team stack, but you increase your probability of winning when you have a good one. Now that we have established that stacking will benefit you in the long run, let’s examine why it is better to employ the team stack strategy on FanDuel rather than on DraftKings.
The first reason that FanDuel is better for stacking is that FanDuel doesn’t adjust player prices according to their matchups nearly as much as DraftKings does. When a team faces a terrible pitcher on DraftKings, the prices of the entire team will be inflated to the point where it is sometimes not even worth picking them. A guy who hits .270 with mediocre power will suddenly cost over $5,000 just because his team is in a great situation. On FanDuel, that player may not even experience a price change at all. The only real exception is when a team goes to Coors field, and in this case FanDuel usually adjusts their prices accordingly. You can see how not adjusting prices for matchups would benefit a team stack because you can now get a bunch of hitters at their normal price against a really bad pitcher. On DraftKings, you might pay $4,000 more than what it would usually cost to get those same four hitters. The lack of price adjustment on FanDuel rewards players who do in-depth research, while DraftKings neutralizes the research to help new players have more success. This is one reason that it is much easier to be a long term profitable MLB player on FanDuel. If you do your research properly, you will find the best plays that don’t cost any more than they usually do on FanDuel, whereas it is often much harder to find good value on DraftKings. The differences in their algorithms are rarely discussed among the DFS community, but we have studied the matter and conclusively determined that FanDuel rewards good research much more in baseball.
The next reason that FanDuel is better for team stacking is that the value of runs, RBIs, and walks is significantly higher. On DraftKings, all three of these stats give you two points. On FanDuel, a walk is worth three points, a run is 3.2, and an RBI is 3.5. If you predict a team will score a lot of runs, these differences are absolutely critical to understand. The baseline stat in baseball is a single. Both sites value this equally at three points. From here, DraftKings values most other stats less than FanDuel. If a team is going to bat around on a pitcher, he likely has bad command. This should result in a fair amount of walks. The relief pitcher that comes in is also more likely to allow walks than the relievers that the team would use if it were a close game in the late innings. The extra point for the walks will add up if your team stack were to each draw one throughout the course of the game. Of course, the most crucial factor is the value of runs and RBIs. If your hitters on a team stack are all getting on base throughout the game, they are almost certainly collecting runs and RBIs. If all of your hitters on different teams are getting on base every time, there is no guarantee that they ever record an RBI or a run. A team stack ensures that if all your hitters are getting hits like your research said they would, you will be rewarded with runs and RBIs. And if you stacked on FanDuel, the value of those runs and RBIs is about 60% greater than if you stacked on DraftKings.
We mostly avoid non-main slates here at DFI. In many sports and in most situations, there is too much variance involved and it’s much harder to be successful in the long run. However, NFL has some nuances with regards to their “extra” slates that can be manipulated to your benefit in DFS. By taking advantage of the Thursday night leagues and the prime time games in an unconventional way, you can actually gain hidden advantages over the masses.
We’ll begin with the Thursday league strategy, which may seem odd at first, but in reality, it makes perfect sense. The main thing to consider is that there is always value in avoiding players with ownership higher than their projection says they should have. When someone joins a Thursday league, they are most likely going to watch that game just because it’s the only football available. In joining that league, they are likely going to target at least one player from that game so they have something to root for in a game they otherwise might not really care about. They are also not as likely to join the league if they aren’t picking anyone from the game that the slate is built around. So how does this knowledge translate to the ownership on these players?
When you consider that there are 13 games in a full NFL week for half the season and 15 for the other half (4 teams on a bye for 8 weeks), you realize that you can’t possibly have a player from every game on your roster. On average, you will have far fewer than one player per game.
Now we examine Thursday games, which have the highest percentage of total ownership relative to the projected scoring total. People force players from the Thursday game into their lineups. Some people force them in to make the Thursday game more fun to watch, and some people look at those players and decide they don’t want them, so they withdraw their Thursday league entry. The end result is that ownership on every single player in Thursday Night Football is higher than it would be if the game were played on Sunday afternoon. Therefore, we join DraftKings leagues specifically on Thursday so that we can still edit our lineup until Sunday morning with the late swap privilege. Regardless of whether you think the Thursday game will be high scoring or low scoring, it is wise to join the leagues and avoid all the players in Thursday night action. Unless everything matches up perfectly for one of the players and you feel like he is a terrific play, you don’t want to have to eat that high ownership. In most cases, you can find equally solid plays in the Sunday games to justify fading everyone on Thursday night.
If you buy into this strategy, enter DraftKings leagues before the leagues lock on Thursday and throw in a random lineup that avoids all the Thursday night players. Then you have a few days to construct your real lineup for the main slate of games, but you’ve likely gained an advantage over all the herd-mentality that goes into the Thursday games.
The next prime-time strategy is for the leagues that begin on Sunday night.…
2015 Record: 44-38
Head Coach – Stan Van Gundy
Additions: Henry Ellenson (Drafted 18th overall – Marquette), Michael Gbinije (Drafted 49th overall – Syracuse), Jon Leuer (Free agent – Phoenix), Boban Marjanovic (Free agent – San Antonio), Ray McCallum (Free agent – Memphis) and Ish Smith (Free agent – Philadelphia).
Departures: Joel Anthony (Free agent – Spurs), Steve Blake, Spencer Dinwiddie (Waived by Detroit and signed by Chicago), Jodie Meeks (Traded to Orlando) and Anthony Tolliver (Free agent – Sacramento).
Studs: The Pistons do not boast an abundance of star power. That being said, Andre Drummond is one of the best young players in the league and is the workhorse for this young Pistons roster (any time you get rid of Joel Anthony and Steve Blake, the roster is going to look younger). Drummond is a rebounding machine and led the league last year with 14.3 per game. Surprisingly he doesn’t block a ton of shots, as he finished 23rd in the league with 1.38 per game. Fun blocked-shots fact: Who finished 12th overall in blocked shots per game last year (ahead of Dwight Howard, Bismack Biyombo, and Andrew Bogut) and is the only listed SF in the top 25 in the category? If you guessed Jerami Grant, you follow random basketball stats to the tee and bravo. Anyway, back to Drummond. Young big men should always be looking to improve their game in any way they can. It doesn’t take a data analyst to see where Drummond needs work. He is a physical specimen that lives above the rim, cleans the boards, is an improving scorer, and yet finds himself among a handful of the league’s big man who shoot below 50% from the free throw line. Drummond found himself well below the 50% benchmark at 35.5% from the charity stripe. Another fun fact: Drummond shot 33% (2-6) from 3pt range last year and only 35.5% from the free throw line. Hmm. He will continue to be the focal point of the Detroit offense especially with Reggie Jackson expected to miss some time at the beginning of the year. Expect to see an uptick from his 24.1 usage rating last year as he matures and grows as a low post presence.
Value plays: After showing some real promise as a 1-2 punch last year, Andre Drummond and Reggie Jackson were looking like a lethal combo for this season. And then news struck that Jackson will be sidelined 6-8 weeks with a knee injury. Everybody panicked for a second when they heard the Jackson news, but then we were reminded that everybody’s favorite fill-in point guard has found his way to Detroit. That’s right, Ish Smith is on the roster. Wherever that man goes, he somehow ends up filling in for a variety of reasons and does a damn good job. It looks like, once again, Ish will have the reigns at least until Jackson comes back and is healthy. We expect him to be as solid as value plays come early in the season and whenever he sees a spot start. He is incredible in the open floor and works well in the pick and roll. If he and Drummond are able to develop some chemistry, Smith will be a very valuable asset. Smith averaged 14.7 points 7 assists and 4 rebounds per game for the 76ers last year and now has plenty of weapons to work alongside. We expect big things out of the Wake Forest product.
Season Outlook: The Pistons will look to build on the momentum that helped them sneak into the playoffs as an 8 seed last season. The injury to Jackson certainly sets them back a bit, but a team couldn’t ask for a better fill-in than Ish Smith. The Pistons struggled to shoot the ball last year. Detroit finished in the bottom third of the league is FG% and points per game. However, in large part because of Drummond, they excelled on the boards, finishing second in the league in rebounding. The addition of Jon Leuer and the continued progression of young players like KCP should help the Pistons eradicate their offensive struggles. The Pistons have promise, but not enough talent to contend for now…even in the East.
In this section of the strategy guide, we will discuss how we view playing time and what it means to your DFS lineups. Many players look at this metric a few different ways, and we will touch on all the approaches and break down how to properly evaluate players’ minutes each night.
First off, a lot of players assume playing time. What we mean by this is that they will check the starting lineup before a tip-off, see that there is an unexpected player starting that day, see him at close to minimum salary, and just plug that player into their lineups. They are assuming that his role as a “starter” means he will be a great pick. Unfortunately, this is often not the case. Most times, it is simply the coaching staff putting a player who normally starts on the bench to sure up the second unit. We see this almost every night in the NBA, which is why the sixth man is so important. To give an example, Luc Richard Mbah a Moute starts most nights at the small forward position for the LA Clippers. He is almost irrelevant for DFS purposes on most nights, but he starts. So an inexperienced DFS player might see this and think he is valuable. However, he only saw around 17 minutes per game last season and averaged about .5 fantasy points per minute; that is not someone that you want to roster in NBA DFS.
Second, other players simply look at minutes per game. A lot of NBA DFS players assume that minutes equal success. For the most part, there is a correlation. If you think about it logically, if a player is on the court more than another player, over the course of the game he will have more opportunity to touch the ball, make plays, and ultimately accrue fantasy points. But, sometimes, it stops right there, at opportunity. For example, Patrick Patterson of the Toronto Raptors averaged nearly 26 minutes per game last year and was the starting power forward for most of the season. He was usually priced around 3.5k-4.5k on FanDuel. These are all positives in Patterson’s favor that would cause many DFS players to use him as a sneaky punt play on any given night. But, Patterson isn’t a great rebounder, doesn’t pass the ball well, and the offense doesn’t really factor him in as a scoring option. All of these traits amount to his lowly 12% usage rate over the course of the season. Based on FanDuel scoring, Patterson averaged about 15 FPTS per game, which at 4k, is just under 4x value. That is somewhat respectable in NBA DFS, but when we look deeper, we see Patterson really wasn’t the kind of player that you should be targeting as a punt option based on playing time. He had 40% of his games go for less than 3x value based on that 4k average price on FD and he only had about 5% of his games go for 6x value or higher. So while there is a correlation between minutes played and higher fantasy points, it isn’t always the end-all-be-all. We can’t assume that just because he will be on the court for at least half the game, he will accumulate fantasy points.
The most effective way to look at playing time is the holy grail of usage rate. Most times you won’t see a huge bump in usage rate for an extended period of time, and you don’t see it happen all that often day-to-day, but it does happen. When it does, you must jump on it. Players usage rates can go up reliably for a few reasons. In most cases, a player is filling in for an injured player or a team is deliberately changing their dynamic to try to create an offensive spark. Sounds a lot the main reasons a player’s minutes might go up too. The beauty of usage rate, however, is that it shows you how much that increase in minutes actually benefits the player. Some guys will get subbed into a starting role just as a placeholder and the other starters will pick up the slack. But finding a player who actually becomes a big part of the offense due to an injury or lineup change can mean huge fantasy value.
You want a player that is capable of multi-category scoring, a player that is involved in the offense (as shown by his usage rate), and a player that can potentially give you that 30 minutes of playing time at a price that allows him to hit an upside range of 5-7x or more. Sometimes these players are fantasy superstars that have been underpriced. Last year, Will Barton was a prime example of this. Barton saw around 29 minutes per game with a usage rate of 23%, and on FD he reached 5x value in 53% of his games. Better yet, he reached 6x value in 37% of his games last season. The FanDuel algorithms just never caught on, because of the fact that he came off the bench and saw limited minutes. The algorithms likely had him as a regression candidate all year, but you could tell by his usage rate that his numbers were legitimate. You can find how often a player hit value last year or this year by using the Rotogrinders Ceiling and Consistency tool. The link below will take you to that:
Other times, players get an opportunity immediately due to a teammates’ injury. For example, last season the Memphis Grizzlies seemingly suited up a D-League team led by Matt Barnes. He proceeded to drop 58 FD points at $4,800 as a DFI Must-Have. He played 44 minutes in an overtime win against the Pelicans and put up a stat line of 26 points, 11 rebounds, and 10 assists. It is games like these that we can predict and capitalize on. That day, you could look at Barnes’ matchup against the Pelicans and see that it would likely be a fast paced game. Barnes was the most experienced player on a 9-man roster, playing for a team that needed a victory to weather the storm of their diminishing playoff chances. However, the main factor here is that all the injuries Memphis suffered freed up the usage rate for guys like Barnes. Usage is a zero-sum game. This means that the five players on the court must create a total usage rate of 100% and if Matt Barnes is the only guy out there who usually does anything, he was a prime candidate to absorb most of that usage. Usage is the reason that Russell Westbrook dominated whenever Kevin Durant was out. The Thunder replaced a guy with a usage rate of 30.2 (9th in NBA) with a guy who almost never touched the ball. This left an incredible amount of usage on the table to be dispersed among the other players. The big mistake DFS players make when a guy gets injured is that they assume the new starter will assume the usage of the original starter. In reality, it often benefits the other good players on the team first.
Now that we’ve explained how important usage rate is and why it should be the main consideration when examining playing time, our next section is all about how to find these usage rates and will explain exactly how far we break things down to understand the way each player involves himself in his team’s offense.
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