The topic of “value” is something you read or hear about on a daily basis when it comes to NBA DFS strategy. The idea is that each player, dependent on their salary, needs to score a certain number of fantasy points to “pay for himself”. It’s a way to make sure that every dollar you spend is being converted into the correct number of fantasy points. The value calculation is fairly simple, but can still be difficult to grasp for new players. I’ll do my best to explain it below:

## The “Value Calculation”

On DraftKings, you get a $50,000 salary cap. How many fantasy points do you need to score from that $50,000? Well, 300 would be a really good goal. Almost all nights, that’s going to be comfortably inside the cash line. If you want to achieve 300 points from $50,000 in salary cap space, you need to get six fantasy points for every $1,000 you spend (300/50 = 6). That means earning 300 fantasy points from $50K in salary is the equivalent of earning 6x value. If you were to score 350 points, that would be 7x value and 250 points would only be 5x value. Those numbers are for your entire lineup, but it makes more sense to break it down on a more individual basis. If every player in your lineup achieves 6x value, you’ll earn 300 fantasy points.

The idea of breaking it down individually levels the playing field between players who cost $10,000 and players who cost $4,000. While it would be awesome if they did, we cannot realistically expect our $4,000 players to score as many fantasy points as our $10,000 players. The value calculation asks both players to do their fair share, which is achieve six fantasy points for every $1,000 in salary. Your $4,000 player only needs 24 fantasy points to pay for himself, while your $10,000 player needs 60 fantasy points to achieve that same value.

## Not All Players Are Created Equal

If you’ve read, watched or listened to anything I’ve ever said, you’ve probably heard me utter “not all players are created equal”. This is where variance comes into play. Some players have extremely volatile fantasy outputs while their counterparts might be more consistent. Here’s a graphic from the Player Scoring Calendar to help explain:

What you’ll notice is that James Harden and Anthony Davis have essentially identical season averages of DraftKings points per game. Harden averaged 59.03 DKPPG while Davis checks in 58.28. On paper, they look like very similar players, but when you look closer, they couldn’t be more different. Harden is much more consistent, scoring between 50-60 DraftKings points on most nights. Davis has flashed higher upside (games in the 80s and 90s), but also has a lower floor. Thanks to duds and/or injuries, Davis is more susceptible to a game in the 30s or 40s which is certainly not acceptable from a player of his salary.

The last column in my Player Scoring Calendar is “StdDev” which is standard deviation. If you’re not familiar with SD, **check it out**, it’s just a mathematical calculation based on a range of outcomes. The more spread out the outcomes, the higher the standard deviation. The closer the outcomes, the lower the standard deviation. Harden’s 9.62 StdDev is one of the lowest in the league, meaning he’s one of the most consistent players in the league. Davis, and his 18.07 StdDev, is one of the most volatile players in the league.

So why does this matter? It matters because the “type” of player you roster should fit with the type of contest that you’re playing. In cash games, you are look for sure points with little risk. You should be looking for consistent players who are not going to kill you with a dud. In GPPs, where a much higher score is necessary to win, you are willing to take on more risk to capture more reward. That’s why you should b thrilled to roster volatile players in an attempt to get their “ceiling game”.

## The Problem With Standard Deviation

I love StdDev and I use it daily. However, while StdDev is a nice snapshot of volatility, it doesn’t tell the whole story. Anthony Davis’ large StdDev really just says that he has games that are very far away from his season average. Well, for fantasy basketball, we don’t care if his games are far away from his average as long as they are ABOVE his average. He could have a game that is 30 points different than his average. If it’s 30 points OVER his season average, that’s great! If it’s 30 points UNDER his season average, we hate him.

## The Solution

That’s where the Value Chart comes into play. Since I have every Game Log for every player this season, I’ve been able to compile how often a player actually hits value. Here are the top 10 players, based on average DraftKings salary.

Now THIS is a chart! Let’s go back to the the Harden vs. Davis example. Now it’s very easy to see the difference between these two players. James Harden, who is a virtual lock to reach 4x value, is going to reach 5x value half the time but he has rarely hit 6x value. Davis has laid some eggs, failing to reach 3x or 4x as often as Harden, but the upside is tremendous. A whopping 36% chance for him to hit 6x and also has games of 7x, 8x and 9x value. This cements the fact that, in general, Davis is a GPP type of player while Harden is better for cash games.

## Fun Facts

- James Harden ranks 316 of 444 of players hitting 6x value.
- LeBron James has the best 6x conversion rate of anyone with the average salary over $6,800. His average salary is $9,600. (This is insane)
- The league average of scoring 5x value is 31.46% while 6x value is 18.4%.

## Now What?

As you dig through this chart, you’ll notice that most of the players who achieve 6x value more often as usually less expensive. This makes sense, right? A guy who cost $5,000 only needs 30 DraftKings points to hit the number while someone like Russell Westbrook might need 72 DraftKings points to reach the same mark. I would argue that it’s okay for your studs to only hit 5x value if your lower salary players can reach 6x, 7x or even 8x value. It’s a give and take. The glaring thing is how insane some players have been this season. LeBron James and Hassan Whiteside are hitting 6x value at ~40% of the time, both with large price tags on a daily basis.

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