Sports Simulation Season & Draft System Designer

## ROLE

You are a sports simulation designer and statistical modeler who has worked on franchise-mode systems comparable to those in Madden NFL, FIFA Career Mode, NBA 2K MyLeague, Football Manager, and Out of the Park Baseball. You understand that sports simulation is fundamentally about two things — authentic statistical modeling that produces realistic outcomes, and compelling management decisions that make the player feel like a real general manager or coach. Your expertise covers player attribute modeling, match simulation engines using probability distributions calibrated to real-world statistics, draft scouting and evaluation systems, salary cap and contract economics, team chemistry mechanics, and the long-term career arcs that make players watch a virtual athlete grow from promising rookie to Hall of Fame legend over a decade of simulated seasons.

## OBJECTIVE

Design a complete season and draft system for a sports simulation game featuring [SPORT: American football / soccer / basketball / baseball / hockey / cricket / rugby / mixed martial arts / esports / fictional sport]. The game covers [SCOPE: single franchise / full league of all teams / international competition / multi-league pyramid with promotion and relegation]. The league has [NUMBER: 8-32] teams with rosters of [NUMBER: 15-53] players each. The season structure includes [COMPONENTS: regular season of X games / playoffs / draft / free agency / preseason / all-star game / international tournaments]. The simulation depth targets [DEPTH: arcade stats-light / balanced realism / hardcore statistical simulation / full analytics with advanced metrics].

## TASK: COMPLETE SPORTS SIMULATION SYSTEM

### Section 1 — Player Attribute Model

Define the complete player attribute system that drives all simulation outcomes. Each player should have [NUMBER: 8-15] primary attributes relevant to the sport. For example, in a basketball simulation: scoring ability (affects field goal percentage calculation), three-point shooting (specifically affects shots from beyond the arc), playmaking (affects assist generation and turnover avoidance), rebounding (affects rebound probability in contested situations), defense (affects opponent's shooting percentage when guarded by this player), athleticism (affects speed, vertical leap, and fast-break opportunities), basketball IQ (affects decision-making in the simulation engine — shot selection quality, help defense positioning, pass timing), durability (affects injury probability per game), clutch factor (modifies performance in high-leverage game situations — final quarter, close score), and potential (hidden ceiling for attribute growth, only partially visible through scouting). Each attribute should range from [RANGE: 1-99] with the distribution following a bell curve — most players cluster around [MIDPOINT: 50-65], stars reach [HIGH: 80-90], and generational talents touch [ELITE: 95-99] in their best attributes. No player should be elite in every attribute — even the best players have weaknesses that create matchup vulnerabilities. Define the position-specific attribute weights that determine a player's overall rating: a point guard's overall is weighted heavily toward playmaking and scoring, while a center's overall emphasizes rebounding and defense. Include [NUMBER: 3-5] hidden attributes revealed only through scouting: work ethic (affects training improvement rate), injury proneness (modifies the base injury probability), leadership (affects team chemistry when this player is captain or veteran presence), media personality (affects fan engagement and merchandise revenue), and loyalty (affects likelihood of accepting team-friendly contracts or demanding trades).

### Section 2 — Match Simulation Engine

Design the algorithm that simulates game outcomes. The simulation should operate at [GRANULARITY: final score only / quarter-by-quarter / play-by-play / real-time with commentary]. For play-by-play simulation, define the possession model: each possession cycles through phases — initiation (which player handles the ball, based on playmaking rating and coaching strategy), action selection (the AI chooses from [NUMBER: 5-10] play types based on situation, player strengths, and opponent weaknesses — in basketball: pick and roll, isolation, post-up, three-point attempt, fast break, off-ball screen, drive and kick, mid-range pull-up), execution (the outcome is determined by comparing the acting player's relevant attributes against the defending player's defensive attributes with a random factor — the formula should be: SuccessProbability equals BaseRate multiplied by (OffenseAttribute divided by (OffenseAttribute plus DefenseAttribute)) multiplied by FatigueModifier multiplied by HomeCourtModifier multiplied by MomentumFactor), and outcome resolution (success generates points, assists, and positive stats while failure generates turnovers, blocks, steals, and missed shots with rebound opportunities). Include the fatigue system where player effectiveness degrades with minutes played — a star player at 90% energy is still better than a bench player at 100%, but at 70% energy the margin narrows, creating meaningful substitution decisions. Design the momentum system where consecutive successful plays increase a team's confidence modifier by [INCREMENT: 2-5%] up to a cap, while consecutive failures decrease it, creating realistic runs and comebacks. Include the injury simulation — each play has a small probability of injury based on player durability, fatigue level, and play intensity, with injuries ranging from minor (miss 1-3 games) to severe (miss months or career-ending).

### Section 3 — Season Structure & Calendar

Define the complete season calendar and competitive structure. The regular season consists of [NUMBER] games per team, scheduled with home and away balance, divisional and conference matchup frequency, and rest day distribution (avoiding back-to-back-to-back games, scheduling rivalry matchups for marquee dates). Include the strength-of-schedule balancing algorithm that ensures no team has an unreasonably easy or difficult path based on opponent quality and travel distance. Design the playoff system: how many teams qualify ([NUMBER: 6-16] out of total teams), the seeding methodology (best record, divisional winners, wild cards, tiebreaker procedures), the playoff format (single elimination, best-of-5 or best-of-7 series, home court advantage allocation), and the championship game or series. Include [NUMBER: 3-5] additional season events: the all-star game (selection based on fan voting, coach selection, or statistical leaders — affects player morale and fan engagement), the trade deadline (a date after which no trades can occur until the offseason, creating urgency for contending teams to make moves), the regular season awards ceremony (MVP, Rookie of the Year, Best Defender, Coach of the Year — determined by statistical leaders with a simulated voting system that occasionally produces controversial picks), and international or cup competition (for sports with parallel competitions that add fixture congestion and squad rotation strategy). Define the offseason structure: end-of-season roster evaluations, coaching changes, draft lottery, draft day, free agency opening, training camp, and preseason games — each with specific game mechanics and timelines.

### Section 4 — Draft System & Scouting

Design the annual player draft that introduces new talent into the league. Define the draft structure: [NUMBER: 1-7] rounds of selections, with pick order determined by [METHOD: inverse record (worst team picks first) / lottery system with weighted probability / complex combination of regular season and playoff performance]. For lottery systems, define the probability distribution — the worst team should have [PERCENTAGE: 14-25%] chance of the first pick while even the best non-playoff team has a small chance, creating hope and drama. Design the draft prospect generation system: each year, generate [NUMBER: 50-250] draft-eligible prospects with varying quality distribution — approximately [NUMBER: 1-3] potential franchise-changing stars, [NUMBER: 5-10] solid starters, [NUMBER: 15-25] rotational players, and the remainder ranging from marginal to undraftable. Each prospect has: true attributes (the actual ratings that will define their career), scouted attributes (the ratings the player sees, which are the true attributes plus or minus a scouting error margin), a scouting report with written analysis (generated from attribute templates — "Elite athleticism but questions about shooting consistency" for a prospect with high athleticism, high potential, but moderate current scoring), college or amateur statistics that may or may not correlate with professional success, and a personality profile that affects how they develop and respond to team culture. Build the scouting system: the player allocates scouting resources (scout assignments, combine attendance, private workouts) to reduce the error margin on specific prospects. Better scouting reveals more accurate attributes and uncovers hidden traits. Poorly scouted prospects are risky picks — they might be steals or busts. Include the draft day trade system where teams exchange picks, players, and future draft selections (creating long-term strategic planning — trading current picks for future assets or vice versa).

### Section 5 — Roster Management & Salary Cap

Define the economic framework that constrains roster construction. The salary cap should be [AMOUNT: define the cap structure] with the following mechanics: hard cap (absolute maximum spending, no exceptions), soft cap with luxury tax (spending above the cap triggers a dollar-for-dollar tax penalty that escalates with repeat offenses), exceptions (specific contract types that allow teams to exceed the cap — veteran minimum, rookie scale, mid-level exception), and minimum spending floor (teams must spend at least [PERCENTAGE: 85-90%] of the cap to prevent tanking through payroll dumping). Design the contract system with [NUMBER: 3-5] contract types: rookie contracts (fixed scale based on draft position, [DURATION: 2-4 years] with team option), veteran contracts (fully negotiable amount and duration, max [DURATION: 4-6 years], with possible no-trade clauses and performance bonuses), max contracts (highest allowed salary for elite players, amount based on years of experience), team-friendly deals (below market value, accepted by loyal players with high team chemistry), and franchise tag or designated player rules (allowing teams to retain one key player above normal limits). Include the free agency simulation where unrestricted free agents evaluate offers based on: total money, role (starting vs bench), team competitiveness (contender vs rebuilder), location preference (big market vs small market, with some players preferring specific cities), and relationship with coaching staff and teammates. Design the trade system with the trade logic AI: AI teams evaluate trades based on win-now vs rebuild strategy, salary cap implications, positional needs, and prospect value. Include a trade fairness check that prevents exploitative deals while allowing smart GMs to win trades through superior evaluation.

### Section 6 — Player Career Progression & Aging

Model the complete career arc of a simulated player from draft to retirement. Define the age-based development curve: players enter the league at age [AGE: 18-22] with current attributes below their potential ceiling. During the development phase (ages [RANGE: 19-27]), attributes improve each offseason based on: the gap between current rating and potential (larger gaps improve faster), work ethic hidden trait (high work ethic improves faster), coaching quality (better coaches accelerate development), role and minutes (players who play more develop faster, but injury risk increases), and off-court events (positive life events boost development, negative events hinder it). During the peak phase (ages [RANGE: 27-32]), attributes plateau at or near potential. During the decline phase (ages [RANGE: 32-40]), physical attributes (speed, athleticism, durability) decline by [RATE: 1-3 points per year] while mental attributes (basketball IQ, leadership) continue to improve or plateau, creating the realistic aging pattern where veterans compensate for physical decline with experience and intelligence. Define the retirement decision: players retire when their overall rating drops below a threshold, they suffer a career-ending injury, or a random retirement event fires based on age and role (a veteran who has been on a minimum contract for three years is more likely to retire than one still starting). Include the Hall of Fame system that evaluates career statistics, championships, and awards to determine induction after retirement.

### Section 7 — Team Chemistry & Coaching

Design the intangible systems that differentiate great teams from collections of talented individuals. Define team chemistry as an aggregate score from [NUMBER: 4-6] factors: roster balance (does the team have the right mix of positions, roles, and skill types — a team of five scorers with no defenders has poor balance), personality compatibility (conflicting personality types reduce chemistry — a ball-dominant scorer and a team-first point guard may clash, while two leaders without enough followers creates power struggles), tenure and familiarity (teammates who have played together for multiple seasons have higher chemistry through learned patterns and trust), coaching fit (the coach's tactical system matches the players' skill sets — a fast-paced coach with slow, defensive players creates friction), and winning culture (teams that win build chemistry, teams that lose erode it — creating the realistic positive and negative spirals). Chemistry affects the simulation engine by modifying team-level performance: high chemistry adds [BONUS: 3-8%] to success probability on plays involving multiple teammates (assists, screens, help defense), while low chemistry reduces cooperative play effectiveness. Design the coaching system with [NUMBER: 3-5] coaching attributes: tactical acumen (affects play-calling quality and game adjustments), player development (affects offseason training improvement for the entire roster), motivation (affects team morale recovery after losses and performance in high-pressure situations), recruitment (affects free agent interest in joining the team), and specialization (each coach has a tactical philosophy — defensive-minded, up-tempo offense, balanced — that creates specific team identity). Include the coaching hire and fire system where the player selects coaches with different strengths, and poor results over [TIMEFRAME: 2-3 seasons] trigger fan and media pressure to make a change.