Analyzing Leadership Messaging Styles and Team Performance in the NFL: Insights from Post-First Loss Press Conferences
Bibliographic record
Abstract
This study investigates the impact of leadership messaging styles used by NFL head coaches on team performance, specifically focusing on messaging following a team's initial loss of the season. The research builds upon previous findings that indicate certain leadership styles, such as Deliberativeness messaging, influence performance throughout the season. However, this study examines the messaging patterns specifically after the first loss. Qualitative data, consisting of press conference transcripts from NFL head coaches after their teams' initial loss between 2020 and 2022, were collected and analyzed using DICTION, a computer-aided text analysis program. This software generated numerical scores indicating the prevalence of different messaging styles: Activity, Optimism, Certainty, Realism, and Commonality. These scores facilitated quantitative tests and correlation analyses. The results reveal that most messaging styles exhibit weak associations with team performance. Nonetheless, the analysis identifies distinct tendencies among coaches, with a propensity for high levels of Optimism, low levels of Certainty and Realism, and moderately high levels of Commonality. These findings suggest that coaches may adopt these messaging patterns due to their effectiveness. Understanding these tendencies can assist amateur coaches in enhancing team performance following the first loss of the season. By leveraging the messaging tendencies identified in this study, coaches can potentially improve team outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".