DFS Terms 101
Glossary of common DFS terms and what they mean—education only.
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Education only • No real-money • No prizes
Tutorials & Guides
Step-by-step learning on DFS concepts: terminology, scoring ideas, roster theory, research workflow, and fair play. No contests or referrals—this is purely educational.
Adults (18+) • Concept-first
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Roster Building Basics
Balance vs. risk, variance, correlation concepts; sample-size thinking.
OpenProjection Pitfalls
Understand uncertainty, biases, and overfitting traps in simple projections.
OpenFair Play Checklist
No scripts, no collusion, no insider misuse; respect & healthy use.
OpenEducational Scoring Ideas
How scoring could be designed; try adjustments as a learning exercise.
OpenResearch Workflow
Signals vs. noise, small-sample traps, and iterative learning loops.
OpenHow-to
DFS Terms 101
Learn the baseline words
Slate: the set of games used in a hypothetical educational example.
Roster: the list of players picked in a learning scenario (no contests hosted).
Variance: how much outcomes might differ from expected values in theory.
Identify conceptual formats
- H2H-style and 50/50-style: safer, consistency-friendly thinking.
- GPP-style: volatile thinking; accepts more swing.
These are learning labels to reason about risk—no contests here.
How-to
Roster Building Basics
Define roles & constraints
Understand positions/roles and the idea of constraints in a learning lineup (e.g., “max 3 from one team”).
Balance vs. risk
Compare a steady archetype vs. a volatile one. Which fits a 50/50-style vs. GPP-style concept?
// Simple illustration for totals (concept-only)
steady = (pts=30, reb=8, ast=6, stl=1, blk=1, tov=2)
volatile = (pts=44, reb=4, ast=3, stl=0, blk=0, tov=5)
How-to
Projection Pitfalls
Small sample traps
Be careful with tiny datasets. Outliers can dominate averages; prefer medians and context.
Bias & uncertainty
- Avoid anchoring on last-game results.
- Add error bars (±) to any estimate.
// Add uncertainty (concept-only)
proj = base ± error_margin
// Example: 28 ± 5 means 23 ~ 33 is reasonable.Workshop
Educational Scoring Ideas
This is a learning-only sketch to reason about trade-offs. Not for real-money use.
// Concept-only scoring
points = (PTS * 1.0)
+ (REB * 1.2)
+ (AST * 1.5)
+ (STL * 3.0)
+ (BLK * 3.0)
- (TOV * 1.0)Try changing one weight at a time; observe how archetypes shift.
How-to
Research Workflow
Signals vs. noise
Document your assumptions. Check whether changes are meaningful or random swings.
Iterate, don’t overfit
Track learnings in a short log. Update slowly; prefer stable patterns over short-term spikes.
Ethics
Fair Play Checklist
- No unfair advantages: no scripts, no collusion, no insider misuse.
- One account. Respect opponents; avoid harassment or toxicity.
- Healthy use: breaks, posture, eye care, and boundaries.
If gaming affects work, relationships, or health, take a break and seek support.
Quick glossary
Key terms at a glance
Slate: The set of games used in a hypothetical educational example.
Roster: The list of players picked in a learning scenario (no contests hosted).
Projection: An estimate used for teaching how people might evaluate performance.
Variance: How much outcomes might differ from expected values in theory.
Download a study checklist
- Read DFS Terms 101
- Do one scoring exercise
- Write a short roster comparison (steady vs. volatile)
- Review the Fair Play checklist
Education only. No contests, no prizes, no referrals.
Compliance reminder
Education-only: These tutorials explain concepts and fair play. We do not host, advertise, or link to real-money contests. No cash language, no prizes, no referrals.
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