Building the “team behind the team”: A 21-month instrumental case study of the Swedish 2018 FIFA World Cup team
Bibliographic record
Abstract
In this study, we provide a novel account of the selection and development of a staff team for the Swedish national men’s football team for the 2018 FIFA World Cup. A total of 37 interviews took place at six time points over a 21-month period to track the values-based steps taken by the national Head Coach to build his support team. The sample was employed in a variety of roles including Head Coach, Assistant Head Coach, Goalkeeping Coach, Mentor to the Head Coach, Performance Analyst, Sports Psychologist, three Scouts, and a Team Manager. The data are presented in a temporal manner and organized according to three core values which were espoused by and later adopted as a value system by the support team: candor, humility, community. The data provide novel insight into the recruitment, formation, and development of the support team underpinned by a value system promoted by the Head Coach using candor, humility, and community. The presentation of longitudinal reflections from the Head Coach and members of the staff team during their build-up to, and excellent performance at the World Cup finals offer a significant contribution to knowledge regarding how a values-led approach was experienced in elite sport. The findings offer salient implications for research and practice.Lay summary: This is a case study of the selection, formation, and development of a support team for the Swedish national football team over 21 months before the men’s 2018 FIFA World Cup. We explored the values-led leadership approach of the Head Coach across this period, presenting data from interviews over time-related to the impact of this values-led approach on the “team behind the team” in the build-up to and during the World Cup.Applied implicationsPsychologists and coaches might benefit from adopting a values-led approach to recruitment and ways of working.The values of candor, humility, and community were effectively articulated and role modeled by the leader and subsequently adopted by members of the support team. Although the specific values may not be suitable, the processes and critical moments that defined this adoption process might be valuable for others to reflect on.Values systems might benefit from “stress testing” over a period of time that is characterized by the relative stability of personnel and consistency of value messaging.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| 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".