DFS Correlation Tool

Last updated: 2026-07-14

Interactive correlation matrix for DFS stacking. See position-pair correlations, win frequencies, and recommended stack configurations for NFL, NBA, and MLB.

Classic QB Stack
72%
QB + WR1 + OPP WR1 is your strongest NFL stack right now.
QB + pass catcher + bring-back (opposing-team pairing). Most common winning GPP (tournament) structure.
Other Recommended Stacks -- NFL

Core Stack = your foundation pairing. Bring-Back = an opposing player added for shootout upside. Game Script = a pairing that wins when your team controls the game.

Game StackStrength: 68%
QBWR1OPP QBOPP WR1

Full game stack for high-total matchups. Maximum ceiling.

RB + DSTStrength: 55%
RBDST

Same-team RB and defense. Benefits from blowout game script.

Naked RBStrength: 45%
RB

RB without stacking. Standalone floor play for cash games.

Why Correlation Matters in DFS

Correlation is the mathematical foundation of DFS stacking. Positively correlated players amplify your upside when game scripts go right. A QB-WR1 stack in a shootout scores far more than the same players would independently. Understanding correlation lets you build lineups with maximum ceiling for GPPs while avoiding negative-correlation traps.

Reading the Correlation Matrix

The heatmap shows correlation coefficients between position pairs. Values range from -1 (perfectly inverse) to +1 (perfectly correlated). Anything above +0.3 is a strong stacking candidate. Click any cell to see detailed context including how often that pairing appears in winning GPP lineups.

Building Optimal Stacks by Sport

NFL stacking centers around the quarterback. The classic QB + WR1 + bring-back structure captures game-script correlation. NBA stacking focuses on pace: players in fast-paced matchups all benefit from more possessions. MLB stacking is about big innings: stack 3-4 hitters from the same team and hope for crooked numbers.

Frequently Asked Questions

What is correlation in DFS?
Correlation measures how two players' fantasy performances move together. A positive correlation (like QB-WR1 at +0.62) means when one scores well, the other likely does too. Negative correlation (like QB-opposing DST at -0.35) means one scoring well typically hurts the other.
What is stacking in DFS?
Stacking is rostering multiple positively correlated players together. The classic stack is QB + WR1 from the same team, because a passing touchdown benefits both. Stacking increases ceiling and variance, making it essential for GPP success.
What is a bring-back in DFS?
A bring-back is a player from the opposing team in your stack, typically an opposing WR1. The logic: if your QB is in a shootout, the opposing team is also scoring. A QB + WR1 + opposing WR1 correlates with high-scoring games.
Should I stack in cash games?
Stacking is generally discouraged in cash games because it increases variance. Cash games reward high floors and consistency. In GPPs, stacking is essential because you need ceiling outcomes to beat large fields.
What is the strongest correlation in NFL DFS?
QB to WR1 has the strongest correlation at approximately +0.62. This makes sense because every passing touchdown scores points for both the QB and the receiver. This pairing appears in about 58% of winning GPP lineups.
Are same-team WRs positively or negatively correlated?
Same-team WR1 and WR2 are slightly negatively correlated at approximately -0.22. They compete for the same targets, so when one has a big game the other often has fewer opportunities. Rostering both reduces your ceiling.
How does MLB stacking differ from NFL?
MLB stacking focuses on same-team hitters who benefit from big innings. A 4-man stack of hitters from the same team is the most common winning GPP structure in MLB DFS. The correlation comes from run-scoring events that benefit multiple hitters in the lineup.
How should I use the correlation heatmap?
Green cells indicate positive correlation (good for stacking), red indicates negative (avoid pairing). Click any cell for detailed analysis including win frequency in GPPs. Use the filter to focus on strong correlations only when building your core stack.

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