Ilayr222/proscout-players-1112

ProScout Players — Synthetic Football Scouting Dataset (1,112 rows)

A clean, fully synthetic dataset of realistic football player profiles built for text → top‑3 player recommendations (ProScout). It’s classroom‑safe (no PII, no scraping), diverse across positions/nations, and structured for retrieval demos.

Core use case: a scout types a brief (e.g., “22–25 left‑footed inverted winger with pace+dribbling in European competition”), the system ranks candidates, and returns the top 3 with bios and attributes.

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1) Dataset Summary

• Rows: 1,112 players
• Source: programmatically generated (no real players)
• Format: CSV (tabular)
• Primary consumer: ProScout (text query → recommendation)

Columns

• name — realistic football-style first + last name
• Nation — country string (e.g., Spain, Brazil)
• position — one of: GK, RB, LB, CB, CDM, CM, CAM, RW, LW, ST
• dominant_foot — Right | Left | Two-footed
• age — integer 17–39
• Goals and Assists — string, e.g. "12G 7A"
• height_cm — integer 165–200
• weight_kg — integer 60–95
• play_style — e.g., Pressing forward, Box‑to‑box, Inverted winger, Regista, Ball‑playing defender, Sweeper keeper…
• key_strengths — 3 strengths (comma‑separated) chosen from: pace, dribbling, finishing, vision, passing, strength, stamina, tackling, positioning, heading, first touch, off the ball, composure, work rate, aggression, long shots, crossing, set pieces, 1v1 defending, interceptions
• league_context — Domestic league | European competition | International
• bio — 2 short sentences auto‑generated from the row fields

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2) Intended Uses

• IR / Retrieval: rank players against a natural‑language brief
• Recommendation: shortlist top‑3 candidates for a role
• Pedagogy: demos of vector search, re‑ranking, rule‑based filters
• Prototyping: LLM prompts and RAG over structured attributes

Not intended for real‑world transfer decisions or media reporting about real players.

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3) Example Usage

Load with pandas

import pandas as pd
df = pd.read_csv("proscout_players_1112.csv")
df.head()

Simple rules + ranking sketch

query = {
    "position": "LW",
    "foot": "Left",
    "must_strengths": {"Pace","Dribbling","Crossing"},
    "age_min": 22, "age_max": 25,
    "league": "European competition"
}

def score(row):
    s = 0
    s += 10 if row["position"] == query["position"] else 0
    s += 6 if query["foot"].lower() in row["dominant_foot"].lower() else 0
    strengths = {x.strip().lower() for x in row["key_strengths"].split(",")}
    s += 3 * len({k.lower() for k in query["must_strengths"]} & strengths)
    s += 4 if query["league"].lower() in row["league_context"].lower() else 0
    s += 2 if query["age_min"] <= row["age"] <= query["age_max"] else 0
    return s

df["score"] = df.apply(score, axis=1)
top3 = df.sort_values("score", ascending=False).head(3)
print(top3[["name","Nation","position","dominant_foot","age","key_strengths","league_context","bio","score"]])

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4) Generation Process (Synthetic)

• Names sampled from common football‑style first/last names.
• Positions, feet, play styles, contexts sampled from curated vocabularies.
• Ages, heights, weights sampled within realistic pro ranges.
• Goals/assists and key strengths randomized but constrained to plausible values.
• Bios programmatically assembled from each row’s attributes.

No external scraping. No real player matching.

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5) Quality, Limitations & Ethics

• Quality: realistic enough for demos; not statistically calibrated to any specific league.
• Bias: distributions are synthetic; do not infer real‑world prevalence from them.
• Limitations: no time dimension, team assignment, injury history, wages, or contracts.
• Ethics: purely synthetic; safe for teaching and prototyping. Avoid portraying rows as real people.

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6) Licensing & Citation

• License: CC‑BY‑4.0 (data and docs).
• How to cite:

  ProScout Players (2025). Synthetic football scouting dataset (1,112 rows). CC‑BY‑4.0.

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7) Maintainers

• Course project: ProScout — European scouting recommender (text → top‑3).
• Contact: add your names / emails or course channel here.