Confusion Reigns: An Analysis of Responses to U.S. Soccer Age Cut-Off Date Policy Change
Bibliographic record
Abstract
Relative age effects (RAEs) have been associated with the common practice of grouping athletes by chronological age. Development and selection advantages are often awarded to those who are born closer to, but following, the cut-off date employed by sport systems. In 2015, the U.S. Soccer Federation announced that it would be changing its birth-year registration cut-off date from August 1st to January 1st. This change was introduced to align the U.S. youth soccer calendar with international standards, and simultaneously provide clearer information on player birthdates to “lessen” RAEs. The magnitude of this policy change has led to considerable controversy, with members of the soccer community taking to social media and website blogs, as well as the U.S. Youth Soccer's website, to voice their opinions and general unhappiness with this decision. Thus, the purpose of this study was to provide a summary of online reactions to the policy change, with attention to the manner in which the U.S. Soccer Federation framed (i.e., the underlying rationale for the decision) and publicly communicated its decision to change the annual cut-off date. Qualitative content analysis was used to analyze data collected from 63 social media sites (websites,n= 43; forums,n= 16; blogs,n= 4). From the 3,851 pages of text derived from these sources, a total of 404 unique passages of text were identified within 262 stakeholder posts. Four categories emerged from the data: stakeholder discussion, outcomes identified by stakeholders, recommended courses of action, and communication regarding the policy change. In general, the actions of the U.S. Soccer Federation and related outcomes were negatively perceived by stakeholders at various levels of the sport. Resistance to the change may have been reduced through enhanced communication from the national level and opportunities for stakeholder input. While one objective of this policy change was to combat RAEs, previous research suggests this organizational change will only shift which group of athletes experience relative age (dis)advantages. There appears to be a disconnect between the academic literature and sport policy with respect to solutions for RAEs, which can lead to unintended consequences for various sport stakeholders.
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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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".