Analyzing Pre-Contracts Agreement in Professional Footballer Contracts in Saudi Arabia: Can Players Change Their Minds?
Bibliographic record
Abstract
FIFA's (The Fédération Internationale de Football Association) regulation and Saudi professional player's regulation allow footballers and their agents to start negotiating new deals with third parties in the last six months of the contracts. Some players and agents may use this period to initiate bidding wars between rival clubs by entering pre-contracts with an alleged possibility of terminating them at no cost. There is growing evidence of such practices in Saudi Arabia after revoking the salary cap rule in professional football contracts. This article analyses this issue through the lens of the existing legal treatment of pre-contracts by FIFA, CAS (Court of Arbitration for Sport), and applicable national laws (Swiss and English). Based on the findings, a series of propositions are made to introduce into the future regulations governing Saudi football leagues. The goal of such propositions is to avoid misapplication of pre-contracts, maintain contractual stability, encourage negotiations in good faith, and promote competitiveness without reducing contractual flexibility for players in the wake of salary cap cancellation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".