DFS Education Center

Correlation and Stacking Concepts

Statistical correlation in DFS refers to how the performance of one player affects the likelihood of another player performing well. Understanding these relationships is crucial for educational purposes.

Positive Correlation Examples

A quarterback and wide receiver from the same team exhibit positive correlation - when the QB throws for many yards and touchdowns, the receiver is more likely to have a good game. Similarly, a running back and his team's defense may correlate positively if the team builds a lead and runs more to control the clock.

Negative Correlation Examples

Players from opposing teams in low-scoring games may exhibit negative correlation. If one team's running back dominates time of possession, the opposing team's skill position players may receive fewer opportunities.

Regression Analysis

Regression analysis helps identify relationships between variables and player performance. Common metrics analyzed include:

• Target share vs. fantasy points
• Red zone touches vs. touchdown probability
• Usage rate vs. consistency
• Opponent strength vs. performance decline

Standard Deviation and Consistency

Standard deviation measures how much a player's performance varies from their average. A lower standard deviation indicates more consistent performance, while higher values suggest more volatile players.

Player Type	Avg Points	Std Dev	Interpretation
Consistent RB	15.2	4.1	Reliable, predictable output
Boom/Bust WR	14.8	8.7	High variance, big game potential
Game Script Dependent	12.5	6.9	Performance varies with game flow

Weather Impact Analysis

Weather conditions significantly affect outdoor sports, particularly NFL games. Educational research examines how different conditions impact player performance.

Wind Effects

Wind speeds above 15-20 mph typically reduce passing efficiency and accuracy. Kickers face increased difficulty with field goals and extra points. Ground games may become more emphasized in windy conditions.

Temperature Considerations

Extreme cold (below 32°F) can affect ball handling, reduce offensive efficiency, and favor defensive play. Dome teams playing in cold weather may face additional challenges due to unfamiliarity.

Injury Impact Studies

Research into how injuries affect player performance, both for the injured player and teammates, provides educational insights.

Usage Rate Changes

When a key player is injured, teammates often see increased usage rates. However, this doesn't always translate to proportional fantasy point increases due to efficiency factors and game script changes.

Home/Away Splits

Statistical analysis of home field advantage reveals varying impacts across sports and teams. Some players perform significantly better at home due to familiarity, crowd support, or travel factors.

Factor	NFL Impact	NBA Impact	MLB Impact
Crowd Noise	High (affects audibles)	Medium	Low
Travel Fatigue	Medium	High (back-to-backs)	Medium
Officiating	Low	Medium	Low
Familiarity	Medium	Low	High (ballpark factors)

Nash Equilibrium in DFS

Game theory concepts help explain optimal decision-making in competitive environments. In DFS, players must consider not just player performance, but how other participants might behave.

The Chalk Dilemma

"Chalk" refers to highly owned, obvious plays. Game theory suggests that if everyone selects the same players, differentiation becomes valuable. However, avoiding good players simply because they're popular can be counterproductive.

Contrarian Theory

Contrarian play involves selecting less popular options to gain ownership advantages. This strategy has more merit in large tournaments where differentiation is crucial for top finishes.

When Contrarian Play Makes Sense

• Large field tournaments with top-heavy payouts
• When chalk players have legitimate concerns (injury, weather, etc.)
• In game stacks where correlation benefits outweigh individual player quality

The Leveling Wars

This concept describes the recursive thinking process: "I think they think I think..." Players attempt to predict what others will do and adjust accordingly, creating multiple levels of strategic thinking.

Cognitive Biases

Understanding psychological biases helps explain common decision-making errors in DFS participation.

Recency Bias

The tendency to overweight recent events when making decisions. A player who scored 30 points last week may seem more attractive despite a season-long average of 12 points.

Confirmation Bias

Seeking information that confirms existing beliefs while ignoring contradictory evidence. This can lead to overconfidence in player selections.

Anchoring Bias

Over-relying on the first piece of information encountered. If a player's salary seems high, participants might anchor to that perception regardless of actual value.

Emotional Decision Making

Emotional states significantly impact decision quality. Frustration from losses may lead to riskier plays, while overconfidence from wins can result in poor bankroll management.

Tilt in DFS

Similar to poker, "tilt" in DFS refers to emotional decision-making after frustrating results. This often manifests as:

• Chasing losses with higher-risk plays
• Abandoning research processes
• Increasing entry amounts beyond normal limits
• Making lineup decisions based on emotion rather than analysis

Primary Data Sources

Effective DFS research requires access to reliable data sources. Understanding the strengths and limitations of different data types is crucial for educational purposes.

Official League Statistics

NFL.com, NBA.com, and MLB.com provide official statistics, but may lack the advanced metrics useful for DFS analysis. These sources are authoritative but sometimes limited in depth.

Advanced Analytics Sites

Sites like Pro Football Focus, Basketball Reference, and FanGraphs offer advanced metrics and analytics. These provide deeper insights but require understanding of the methodologies used.

Beat Reporter Information

Local beat reporters often provide the most timely information about injuries, depth chart changes, and team news. Social media has made this information more accessible but also more volatile.

Vegas Lines and Totals

Betting lines reflect market expectations and can provide insights into expected game scripts, scoring environments, and player prop bets.

Data Type	Reliability	Timeliness	Depth
Official Stats	High	Medium	Medium
Advanced Analytics	High	Low	High
Beat Reporters	Medium	High	Low
Vegas Lines	High	High	Medium

Payout Structure Analysis

Different contest types require different approaches based on their payout structures. Understanding these differences is fundamental to making informed decisions.

Flat Payout Structures (50/50s, Double-Ups)

These contests pay roughly half the field the same amount. The optimal approach emphasizes consistency and minimizing downside risk rather than maximizing upside potential.

Top-Heavy Structures (GPPs)

Large tournaments with most prizes going to top finishers require a different approach. Higher variance plays and differentiation from the field become more important than raw point totals.

Field Size Considerations

The number of entries in a contest affects optimal decision-making. Smaller fields require less differentiation, while massive tournaments necessitate unique roster construction.

Multi-Entry Strategy

Many contests allow multiple entries per participant. This creates additional strategic considerations around correlation, diversification, and bankroll allocation across lineups.

Kelly Criterion Application

The Kelly Criterion is a mathematical formula used to determine optimal bet sizing based on perceived edge and bankroll size. While complex to implement perfectly in DFS, the underlying principles are educational.

Kelly Formula Basics

f = (bp - q) / b, where f = fraction of bankroll to wager, b = odds received, p = probability of winning, q = probability of losing. This formula helps determine theoretically optimal bet sizes.

Risk of Ruin Calculations

Risk of ruin refers to the probability of losing your entire bankroll. These calculations help determine sustainable play levels and the importance of proper bankroll management.

Variance and Sample Sizes

DFS results have high variance, meaning short-term results can differ significantly from long-term expectations. Understanding required sample sizes for meaningful analysis is crucial.

Metric	Small Sample (10 contests)	Medium Sample (100 contests)	Large Sample (1000+ contests)
Win Rate Accuracy	Low	Medium	High
ROI Reliability	Very Low	Low	Medium
Strategy Evaluation	Unreliable	Preliminary	Meaningful

Platform Innovation

DFS platforms continue evolving with new contest types, features, and user experiences. Understanding these trends helps participants make informed decisions about where and how to play.

Pick'em Style Games

Newer formats like pick'em contests simplify the experience by having users select higher/lower or more/less on player props rather than building full lineups.

Single-Game Contests

Contests focused on individual games rather than full slates have grown in popularity, offering different strategic considerations around game script and correlation.

Regulatory Evolution

The regulatory landscape continues evolving, with more states considering legalization and regulation of DFS. This affects market access, consumer protections, and industry practices.

Integration with Sports Betting

As sports betting becomes more widely legal, many platforms are integrating DFS with traditional sportsbook offerings, creating new products and cross-promotional opportunities.

Technology Advances

Improved mobile experiences, faster data processing, and enhanced analytics tools continue to evolve the DFS landscape. Live scoring, real-time updates, and social features enhance user engagement.