---
language: en
license: cc-by-4.0
pretty_name: ProScout Players (Synthetic Football Scouting Dataset)
size_categories: 1K<n<10K
task_categories:
- information-retrieval
- recommendation
- text-classification
- tabular
tags:
- football
- soccer
- scouting
- synthetic-data
- education
---

# 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.

---

## 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  

---

## 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.

---

## 3) Example Usage

### Load with pandas
```python
import pandas as pd
df = pd.read_csv("proscout_players_1112.csv")
df.head()
```

### Simple rules + ranking sketch
```python
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"]])
```

---

## 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.

---

## 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.

---

## 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.

---

## 7) Maintainers

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