Introduction
Daily Fantasy Sports (DFS) platforms like DraftKings, FanDuel, and Underdog Fantasy rely heavily on algorithms to project player performance, determine salaries, detect unfair behavior, and help users build competitive lineups. Understanding how these algorithms work gives players an advantage when identifying value plays, low-owned gems, and potential breakout performers.
1. Player Projection Algorithms
Projection algorithms estimate how many fantasy points a player is expected to score. These models use a combination of statistics, contextual information, and machine learning.
What Goes Into Player Projections
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Recent form and historical performance
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Expected workload (minutes, targets, carries, usage)
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Opponent matchup and defensive efficiency
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Game environment (pace, Vegas total, weather)
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Injuries and role changes
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Team strategy or coaching tendencies
These inputs feed into regression models, machine learning systems, and probabilistic simulations.
Real Example
NFL Running Back Projection:
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Expected carries: 18
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Expected targets: 4
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Yards per carry: 5
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Touchdown probability: 0.70
Projected fantasy points:
(18 × 0.1) + (4 × 1) + (90 rushing yards = 9 pts) + (0.7 × 6)
= 1.8 + 4 + 9 + 4.2
= 19 fantasy points
2. Salary Algorithms
Salary algorithms determine the cost of each player based on projection, volatility, matchup, and popularity. Salaries ensure balanced contests and prevent everyone from stacking elite players.
Factors That Influence Salary
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Higher projection → higher salary
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Soft matchups → salary bump
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Injuries increasing workload → price increase
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Inconsistent or risky players → lowered or unstable salary
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Slate size (smaller slates = more expensive stars)
Real Example
An NBA star projected for 55 fantasy points might cost:
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$11,400 on a large slate
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$12,500 on a short slate (fewer alternatives)
If returning from injury with limited minutes:
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Salary may drop to $10,200 despite high ability.
3. Ownership Projection Algorithms
DFS isn’t only about scoring the most points—it’s also about being different in tournaments (GPPs). Ownership algorithms predict how popular each player will be.
Inputs for Ownership Models
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Recent performance and media buzz
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Salary bargains
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Projections from major DFS sites
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Matchup quality
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Public sentiment and recency bias
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Slate size and positional scarcity
Real Example
If a wide receiver is underpriced due to a matchup with a weak defense, ownership may project at 35–45%, signaling chalk. Savvy DFS players may pivot to a similar but lower-owned player.
4. Lineup Optimizer Algorithms
DFS optimizers use mathematical programming—often linear or mixed-integer optimization—to build the best lineup combinations within salary and positional constraints.
What Optimizers Consider
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Maximum projected fantasy points
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Salary cap rules
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Stacking rules (QB + WR or Team Stacking)
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Minimum or maximum ownership exposure
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Positional flexibility
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Correlation and leverage strategies
Real Example Lineup (DraftKings NFL)
Optimizer’s top lineup might produce:
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QB: 24.3 pts
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RB1: 21.1 pts
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RB2: 18.9 pts
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WR1: 23.0 pts
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WR2: 14.2 pts
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WR3: 13.8 pts
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TE: 12.5 pts
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FLEX: 15.7 pts
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DST: 8.4 pts
Total projection: 152 projected points
5. Contest Fairness & Anti-Collusion Algorithms
DFS platforms rely on monitoring tools to ensure compliance and fairness.
What They Detect
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Duplicate lineups from multiple accounts
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Collusion or shared data patterns
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Bot-based mass entry
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Unusual win-rate spikes
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Suspicious late-swap behaviour
Real Example
If two players submit nearly identical 150-lineup sets from adjacent IP addresses, the system flags it for review.
6. Live Updating Algorithms
During games, DFS platforms use real-time data feeds and models to update scores immediately.
What Live Algorithms Track
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Real-time stats (yards, points, rebounds)
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Bonus conditions (double-doubles, 100-yard games)
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Adjustments for stat corrections or replays
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Injury events that alter projections
Some advanced showdown or live DFS formats also use dynamic expected value updates.
Conclusion
Daily Fantasy Sports algorithms combine advanced mathematics, real-time data, and user behaviour modeling to shape projections, salaries, ownership, and lineup building. By understanding these systems, DFS players can make more informed decisions, spot underpriced players, and build lineups with both high upside and strategic leverage.