Race to the podium: separating and conjoining the car and driver in F1 racing
Bibliographic record
Abstract
This paper provides a statistical estimate of the breakdown in race outcomes in Formula One races between the two most important inputs: driver skill and car technology. Financial data and racing results from the 2012–19 F1 seasons are used to estimate a combined driver and team fixed effects FGLS regression model for each season. Treating each season uniquely allows for the exclusion of weather and track specific variables common to other statistical studies of F1 racing. Our use of financial data provides an answer to the economic question of how should F1 teams allocate their scarce financial resources. The so-called “80-20” rule distinguishing team effects and driver effects is found to be a very rough approximation to the output shares for teams and drivers. A strong complementarity exists between driver skill and car technology that distorts the rule. The return to driver salaries and team budgets are both positive in term of race outcomes, but at diminishing rates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".