yanivohayon1/transfertalk-players

TransferTalk Players Dataset

A fully synthetic, fictional multimodal dataset of 1,000 football (soccer) player profiles, created for a university Data Science capstone project. Each profile pairs a rich text description with a matching illustrated player card image.

No real players, teams, or leagues are represented. All names, teams, leagues, and nationalities belong to a fictional universe ("The Meridian League"), generated to avoid any real-world IP, privacy, or hallucination-about-real-people concerns.

Dataset Summary

• 1,000 rows, 30 columns
• Modality: text + image (multimodal)
• Generation method: local open-weight LLM (Qwen/Qwen2.5-1.5B-Instruct) for text, stabilityai/sdxl-turbo for images, both run via transformers/diffusers on a Colab GPU. No scraping, no human-written content, no formula/random substitution for the generated content itself.

Schema

Column	Type	Description
team	string	Fictional club name
league	string	Fictional league name
nationality	string	Fictional nationality
position	string	Playing position (GK, CB, FB, DM, CM, AM, W, ST)
style	string	Playing style archetype (e.g. "Poacher", "Sweeper Keeper")
age	int	17-38
first_name, last_name, full_name	string	Fictional player name
bio	string	Short fictional biography (LLM-generated)
strengths, weaknesses	list[string]	LLM-generated
scouting_report	string	LLM-generated scouting text
pace, shooting, passing, dribbling, defending, physical	int (0-99)	LLM-generated skill ratings
market_value_eur	float	Derived (not AI-generated) - computed from stats + age via a deterministic formula, analogous to standard feature engineering
player_id	int	Sequential identifier
image_path	string	Relative path to the matching player card illustration
height_cm	int	Derived - position-aware, deterministic per player_id
preferred_foot	string	Derived - right / left / both
squad_number	int	Derived - unique within each team
contract_expires	string	Derived - DD/MM/YYYY
market_value_history	list[{season, value_eur}]	Derived - 5 seasons, same formula as market_value_eur applied per past age
appearances, goals, assists	int	Derived - current-season stats, Poisson-sampled from position/skill-weighted expected values

Generation Methodology (summary)

Profiles are generated in batches of 10 via structured JSON prompting, using a randomly sampled few-shot example per batch to guide format and writing style. A brace-depth JSON salvage routine recovers valid objects from partially malformed model output instead of discarding the whole batch. Player card images are generated per-profile from a prompt built out of the player's own position/style/team-color attributes, using a 2-step SDXL-Turbo pipeline.

Additional Fields (Feature Engineering)

Beyond the original AI-generated profile (bio, scouting report, 6 skill stats), the dataset includes deterministically-derived fields, computed from the existing generated data (no additional LLM calls):

• height_cm, preferred_foot, squad_number, contract_expires - profile facts, position-aware where relevant (e.g. taller height ranges for GK/CB).
• market_value_history - 5 seasons (2022-2026) of market value, computed with the same formula as market_value_eur, applied to each season's effective age, plus small random jitter for a realistic trend.
• appearances, goals, assists - current-season stats, sampled from a Poisson distribution over an expected value tied to existing skill stats (shooting→goals, passing→assists, physical→appearances) and a position-specific weight (e.g. strikers score far more than goalkeepers).

These fields exist to bring the player profile closer to a real scouting platform's layout, while keeping the actual generative AI component (the text bio, scouting report, and later the comparison reports) as the project's core GenAI focus - these numeric extensions are feature engineering, not AI-generated content.

Exploratory Data Analysis - Summary

Full EDA notebook is included in this repository (EDA_notebook.ipynb). As required, AI-generation issues were actively identified and either corrected or explicitly flagged:

1. Duplicate full names (621/1000, initial): name pool of 20x20=400 combinations was far smaller than 1,000 samples drawn with replacement (birthday-paradox collision). Fixed by expanding the pool to 46x46=2,116 combinations.
2. Incomplete records (32/1000): some LLM responses were truncated mid-JSON, producing records missing required fields. Fixed by dropping and regenerating.
3. Duplicate full names re-introduced (31/1000): discovered during EDA (df.describe() showed full_name uniqueness of 969, not 1000) because the regeneration in fix #2 sampled from the original small name pool. Fixed the same way as issue #1 - a direct example of iterating generation based on EDA.
4. Stat value range: despite requesting 0-99, the model never produced values at the true extremes (observed range: 4-98 across all 1,000 profiles). Flagged, not corrected - a mild instruction-following gap.
5. Goalkeeper stat modeling: goalkeepers (GK) show the highest average defending of any position, since the model reused the same 6-stat schema for every position rather than goalkeeper-specific attributes. Flagged as a schema-design limitation.
6. Weak text-to-number alignment: profiles whose scouting_report uses superlative language ("world class", "exceptional") average only ~6.4% higher market_value_eur than those without it - correct direction, weaker signal than expected.
7. Age-38 distribution check: age 38 shows a noticeably higher count (61) than the mean across other ages (44.7, expected ~45.5 under uniform sampling) - a ~2.3 standard-deviation deviation. Reviewed and judged to be within normal random noise across 22 age categories rather than a systematic bug.

Stat correlations were also checked for internal consistency (e.g. shooting vs defending: -0.74; physical vs defending: +0.59), supporting that the LLM generally preserved sensible relationships between position and skill ratings.

Files in this repository

• transfertalk_players_raw.json, transfertalk_players_raw.csv - the dataset
• player_cards/ - 1,000 player card images ({player_id}.png)
• Synthetic_Data_Generation.ipynb - Part 1 generation notebook (includes a feature-engineering extension section for the additional fields above)
• EDA_notebook.ipynb - Part 2 EDA notebook (this summary's full source)

Intended Use

Built for the TransferTalk capstone project - a Transfermarkt-style multimodal demo app (recommendation system + AI-generated scouting reports). Suitable for teaching/demoing multimodal embeddings, recommendation systems, and LLM-based synthetic data generation pipelines.

License

MIT. All content is synthetic and fictional.