*For full access to our team charts and player distributions for NBA, NFL, and MLB DFS, become an Advanced Sports Analytics member here.*

In addition to the release of our new NBA Player Fantasy Projection App, Advanced Sports Analytics will also be rolling out our traditional content offering for optimizing your NBA DFS strategy. The goal of this content is to provide subscribers with the tools needed to gain an additional edge in their play, particularly in leveraging data to identify scenarios in which specific players gain and lose value that the general DFS population is less attuned to.

At the moment, our NBA content offerings are outlined below.

- Player Distributions: Fantasy Points, Salary, Fantasy Points/$1K Salary
- Player Points vs. Spread and Actual Points Charts
- Fantasy Points vs. Opponent Average Fantasy Points Allowed
- Player Correlation Matrices
- Player Proportion Boxplots

**1. Player Distributions**

From its inception, ASA has always placed an importance of evaluating players potential not as a single value, but as a distribution or probability range of values. We provide 3 core distributions for every NBA player.

- The first is a distribution of players' fantasy points scored on a night-to-night basis.

These distributions can be valuable in evaluating the maximum upside and risk of players by providing the range of scores a player tends to produce, and how probable point totals within that range are. - The second set of distributions we provide is a distribution of players' past salaries.

These are best used in evaluating the relative cost of players on any given night. It can sometimes be difficult to determine if players' nightly prices are good bargains or not, but by consulting our distributions of a player's previous salaries you can easily discern where a nightly salary falls on the spectrum of a player's past prices. - The final set of distributions we offer are distributions of players' fantasy points scored per $1K salary.

These distributions can be used in identifying players that are frequently undervalued or overvalued by DraftKings and can help with targeting players with high value upside potential.

**2. Player Points vs. Spread and Actual Points**

We have always preached the importance of relying on pre-game metrics such as implied point totals and spreads as means of targeting high upside players. These charts visualize which players are best for targeting under certain circumstances. When analyzing Fantasy Points vs. Team Point Total charts, we generally advise targeting players with steep slopes in games with high implied point totals and warn against targeting such players in games with low implied point totals, perhaps seeking out players with flat or negative slopes.

In the example above, Kyrie Irving would make for an ideal target for a game in which the Celtics are expected to put up big numbers, while Al Horford might make for a better target in games which the Celtics are expected to score around 100 points.

A similar strategy can be applied to our Fantasy Points vs. Game Spread charts. Players with steep and negative slopes should be targeted in games in which their team is expected to win while they might have more risk when their team is the underdog. These players tend to be star players, as they frequently perform better in wins, and get more minutes. However players with positive slopes are equally valuable to identify. These players tend to do better in losses, perhaps due to increased minutes or usage in games which their team waves the white flag.

In the above chart for the New Orleans Pelicans, Anthony Davis and DeMarcus Cousins are clearly more productive in winning efforts. However, when the Pelicans lose (especially by a wide margin), their production drops off, and secondary players like Jrue Holiday and E'Twaun Moore become more productive.

**3. Fantasy Points vs. Opponent Average Fantasy Points Allowed**

Targeting players going up against weak opponents is usually a good strategy. Our Fantasy Points vs. Opponent Average Allowed charts are a useful tools in evaluating how heavily to lean on this strategy for certain players. Players with steep slopes tend to do much better against weaker opponents, while players with flat slopes are pretty consistent regardless of the quality of their opponents defense. Players with negative slopes are the anomalies who actually do worse against weaker opponents.

The Y-variable for these charts are pretty straight forward, they are the fantasy points scored by each player in a game. The X-variable requires some more explanation. These are the average fantasy points allowed by the players' opponent in each game to the position (or average of positions for players that qualify for multiple positions) that each player qualifies for. So since Karl-Anthony Townes qualifies as a Center and Power Forward, a data point for KAT's game against the Suns would have an X-value equal to the average of the average fantasy points allowed by the Suns to Centers and Power Forwards in the in their previous. The Y-value is equal to the number of fantasy points scored by KAT in that game.

**4. Player Correlation Matrices**

These charts are used for optimizing your stacked lineup selections. Many fantasy players will target a stack of players on the same team, hoping to cash in on specific team putting up high-scoring night, resulting in correlated player putting up big fantasy numbers together. Players with strong positive correlations make for good stacking candidates. Conversely, players with negative correlations could be targeting as optimal fade options or optimal players to play when the other player in the correlation pair is injured.

In Houston's correlation matrix above, we can see that Clint Capela makes for a compelling stack with James Harden or Eric Gordon, as they tend to have strong positive correlation. However, if Capela is injured, targeting guys like Chris Paul or Luc Mbah a Moute might be advised.

**5. Player Proportion Boxplots**

An alternative way of projecting a player's fantasy value is by attempting to project the overall fantasy production of his team, and then estimating the proportion of the team's fantasy points he can be expected to account for. Our Player Proportion Boxplots chart the 25th percentile, median, and 75th percentile of the proportion of team fantasy points each player accounts for. These can also help contextualize the upside and risk of player beyond the distribution charts.

In general, players with larger boxes above the median (the middle bar of the boxes) i.e. Blake Griffin, Lou Williams, and Austin Rivers have more upside, while players with larger boxes below the median i.e. DeAndre Jordan have more risk. Players with tight boxplots i.e. Griffin and Wes Johnson are generally more consistent, while players with wider boxplots i.e. Lou Williams and Danilo Gallinari are more inconsistent (for better or worse).