Fantasy Sports: A Game for Money and Strategy

Introduction: why fantasy is maths + game theory

Fantasy sports for money is not a guessing game, but a combination of data, probability and risk management. You build a virtual roster of real athletes and earn points according to league/court rules. In short-term formats (DFS, daily fantasy), dispersion and game theory are more important, in seasonal formats, the forecast of stable shape and depth of the roster. Below is a practical system that helps you play plus at a distance.

1) Fantasy formats and how they differ

DFS (Daily Fantasy Sports): One-day/single "slates." Most often salary cap. Suitable for lovers of models, correlations, late swaps.

Seasonal leagues (season-long): draft at the beginning, managing the roster during the season (trades, divers). The value is in the forecast of stability and depth.

Best Ball: Draft once, then the platform automatically counts the best players in each week. Strategy is the priority of the upside and correlations for the playoffs.

Snake Draft/Auction: different distribution mechanics: snake - in turn "snake," auction - budget for bets for players.

Single Game/Showdown (DFS): One-match roster, enhanced roles (captain/MVP with point multiplier) - It's critical to catch correlations correctly.

2) How to count points: scoring affects strategy

Typical parameters: goals/assists, throws, targets/receptions, yards/touchdowns, three-point/rebounds/assists, wedges, saves, etc.

Conclusion: for a specific scoring, the "value" of positions and the risk profile change. For example, where performative metrics (targets, usage) are valued, there you can get ahead of the market in underestimated roles, even if the "raw" points are still low.

3) Composition construction: framework + correlations

Key principles:

Basic projection of points (median projection) for each player.
Range of outcomes: median ≠ ceiling; in tournaments (GPP) you need an upside.

Correlations ("stacking"):

Football: Quarterback + receiver (s) + run-back from an opponent.
Basketball: we play tempo matches, we stack P&R bundles.
Hockey: links and special teams of the majority.
European football: ligaments "playmaker → forward," cross + "target man," corner + center backs.
Negative correlations: for example, in football, the goalkeeper and the opponent's striker.
Leverage: Players who give a comparable upside with a lower ownership.

4) DFS game theory: how to beat the field

Cash vs GPP:

Cash (50/50, head-to-head): minimize risk, take high medians and stable roles.
GPP/tournaments: aim for the upside, look for low-owner combinations, allow more variance.
Ownership and cap: avoid too "chalky" (most popular) assemblies without leverage.
Late swap: Leave flexibility in late match slots to avoid duplicating other people's lineups.
Correlations at the game/match level: pace, total, weather conditions (for the outdoor), injuries and role redistribution.
Overlay: Tournaments with a non-good prize pool give a mathematical advantage - all other things being equal, play them.

5) Sources of benefits: data and signal

Role volume (usage): minutes/snaps/targets/throws/strikes - a predictor of future points over "effective" metrics.

Lynap news and injuries: redistribution of volume in the last hour before the deadline is the main source of value.

Matchup and pace: Fast teams/high total raises ceiling; low rate - media boost for "cache."

Special teams/standards: penalties/penalties, corners, most - free "points" roles.

Regression to average: Excessive "cold/heat" periods are often rolled back.

6) Modeling: from simple to advanced

Quick Start:

Basic projections to the player (median) from the recent volume + minutes/snaps; adjustments for the total match and favoritism.
Regressions for positions: expected points = f (usage, pace, role in standards/bonuses).

Intermediate:

Bayes update role and efficiency (sliding window).
Correlation module: players of one team/link, QB-WR, PG-C, bundles of special teams.
Multi-objective compositional optimization: median + variance + ownership/correlation constraints.

Advanced:

Event log/trekking (if available): xG/xA, shot quality, pace-adjusted positions.
Monte Carlo simulation of slate (1000-5000 runs) → distribution of roster points, probability of minkash/top 1%.
Ensambly (gradient boosting/neural networks) + manual "finger rules" for news.

7) Tournament selection and bankroll

Staking: 0. 5-2% of the bank for slate (in total for all lines).

Portfolio for slate: 60-80% cash if stability is desired and 20-40% GPP; aggressive style is the opposite.

Multiple rulers: in GPP it is better to make several options with different kernel and leverage.

Rake and payout structure: if payout (more prizes) - lower variance; top heavy - higher upside, but higher risk.

Exposure limits: no more than 20-30% of the bank for one match/team/stack.

8) Strategies for different sports (briefly)

American Football: QB + WR/TE stacks, return from opponent; weather factors; red zone usage.

Basketball: Minutes and pace are king; valuable later removal/starts of "backups."

Hockey: links and majority; goalkeeper against "low total" and a large volume of shots.

Football (soccer): standards/penalties, crosses and corners; in Showdown, we accompany the captain with assistants.

Baseball: Correlations inside the lineup, parks/wind, pitchers vs. strikeout lineups.

Esports (DFS): map-pools/drafts, objects (Dragons/Baron/Roshan), pistol.

9) Tools and Processes

Projections/Ownership-estimates: summarize several sources + your adjustments for news.

Composition optimizer: helps to take into account cap, stacking and restrictions.

Results tracker: CLV by projections (composition shift vs field), ROI by formats and sports.

Deadline checklists: fig reports, starting lineups, weather, totals/lines, late swaps.

10) KPI and quality control

ROI by slates/sports/formats.

Hit Rate in cash and top 1 %/top 5% hit in GPP.

Exposure discipline: meeting player/stack limits.

Accuracy of projections: mean error (MAE/MAPE) and calibration of ranges.

11) Frequent errors

1. Ignore ownership: in GPP "chalk" trains without leverage rarely win TOP prizes.

2. Revaluation of the "form" without a role: a hot series without stable minutes/snaps breaks down first.

3. No late swaps: Especially in the NBA/NHL with "last hour" news.

4. Same strategy for cash and GPP: Blending targets reduces EVs of both.

5. No bankroll plan: excessive bets on "favorite" games/teams.

12) Responsible play and ethics

Set weekly/monthly time and loss limits.

Avoid "dogons" after a bad slate.

Use bonuses and free sign-ins, but don't increase the risk because of "feeling free."

Play only where participation for money is permitted by the law of your jurisdiction and observe age restrictions.

13) Check list before deadline (short)

1. Confirmed starting lineups/minutes/snaps?

2. Updated projections for news, weather, totals?

3. Are there stacks/correlations and counter-stacks with leverage?

4. Is the late swap plan ready?

5. Bankroll and exposures meet limits?

Fantasy sports for money is a discipline: data → projections → correlations → game theory → risk control. Start with clear bankroll rules, divide the strategy into cash and GPP, collect your own projections and practice late swaps. Over time, your process will give a stable ROI, and the dispersion of slates will cease to control emotions.