Athlete External Loads Across a Collegiate Men's Lacrosse Season
Bibliographic record
Abstract
ABSTRACT: Fields, JB, Jones, MT, Feit, MK, and Jagim, AR. Athlete external loads across a collegiate men's lacrosse season. J Strength Cond Res 37(8): e455-e461, 2023-Tracking and quantifying athlete workload may provide insight into best practice periodization strategies and workload management. Limited information exists detailing the external demands associated with collegiate men's lacrosse. Therefore, the purpose of this study was to investigate and compare practice, game, and positional differences across a lacrosse season. Male collegiate lacrosse athletes ( n = 17; body mass: 79.9 kg ± 9.7 kg; body fat %: 14.7 ± 3.8%) wore a global positional system device during practices ( n = 66) and games ( n = 19). External load metrics were total distance (TD), sprint distance (SD), accelerations (>3 m·s -2 ), sprint efforts, player load (PL), PL/min, and top speed. Multiple analysis of variances assessed differences in external loads across session (practice versus game) and sport position (attackers, midfielders, and defenders). A 3 × 4 analysis of variance was used to determine a position-by-time (first quarter [Q1], second quarter [Q2], third quarter [Q3], and fourth quarter [Q4]) interaction ( p < 0.05). Total distance ( p < 0.001, η2 : 0.047), SD ( p < 0.001, η2 : 0.093), sprint efforts ( p < 0.001, η2 : 0.077), PL ( p < 0.001, η2 : 0.022), and top speeds ( p < 0.001, η2 : 0.086) were all higher in games compared with practices. Attackers and defenders covered more TD ( p < 0.001, η2 = 0.0257), accelerations ( p < 0.001, η2 = 0.126), PL ( p < 0.001, η2 = 0.233), and PL/min ( p < 0.001, η2 = 0.193) than midfielders. Attackers and midfielders covered more SD ( p = 0.002 η2 = 0.043), sprint efforts ( p < 0.001, η2 = 0.053), and achieved higher top speed ( p < 0.001, η2 = 0.0063) than defenders. Volume and intensity metrics declined by Q4 in attackers and defenders ( p < 0.05), whereas external load measures did not differ across quarters for midfielders. Monitoring in-game external loads can assist coaches with individualizing training programs and preparing athletes for game demands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".