Match Running Performance of Brazilian Professional Soccer Players according to Tournament Types
Bibliographic record
Abstract
The present study aimed to report different level games in terms of their external game loads, using data collected from GPS performance indicators in Brazilian soccer teams. We used measures of 464 performances of professional soccer athletes during the National Tournament (NT=265), State Tournament (ST=89), National Cup (NC=44), and the International Tournament (IT=66). The performance analysis included the assessment of Total (meters) and Relative (meters/minutes) distances; running (>14km/h), and sprinting (>18km/h) distance; the number of sprints (>18km/h and >24km/h); accelerations (above three m/s2), deceleration (less than three m/s2) and jumps (>30 cm); Total and Relative load – per minute. There were differences (p<0.05) in terms of relative distance between NT and ST (102.2 ±9.5 vs. 98.1± 10.3) and between ST and NC (98.1± 10.3 vs. 103.4 ±9.6). In sprints >18km/h NT differed from ST (60.4 ±5.9 vs 52.7 ±19.9). In sprints >24km/h differences could be found between NT and ST (10.7 ±5.9 vs 8.7 ±5.4). In Total Load NT differed with respect to ST (908.6 ±141.5 vs. 852.7 ±138.5) In Relative Load differences were reported between NT and ST (10 ±1.2 vs. 9.3 ±1.4) and IT (10 ±1.2 vs. 9.4 ±1.4), and between ST and NC (9.3 ±1.4 vs. 10.0 ±1.4). Finally, concerning deceleration, NT differed when compared to ST (36.1 ±9.9 vs. 32 ±11) as well as ST differed from IT (32 ±11 vs. 37.5 ±9.7). The present results make it possible to create specific training games according to tournament level associated with the predominant activities performed during the competition.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".