FileDescription
Data_SeasonLong_Anonymous.xlsx
SevenFormulas_MasterScript.m - MATLAB analysis script reproducing all results and figures

Cohort

Sport: Male professional soccer (English Premier League)
Season: 2019–20 competitive season
Players: 17 (age: 27.6 ± 5.9 years; height: 181.9 ± 7.5 cm; body mass: 78.5 ± 7.6 kg)
Position groups: Goalkeepers (n = 4), Defenders (n = 7), Midfielders (n = 5), Forwards (n = 1)

Season duration: 36 calendar weeks (weeks 1–36; week 28 absent due to scheduling)
Testing weeks: 35
Total rows in dataset: 601 (includes all testing occasions per player)
Valid bilateral adduction observations used in analysis: 429

Observations per player ranged from 14 to 35 (players with fewer than 20 observations were retained in the main dataset but may be noted as having limited stabilisation data in supplementary analyses).

Variable Descriptions
ColumnVariableDescription
A: Player_IDAnonymised player identifier (P01–P17)
B: DateRemoved (blank)
C: ParticipantNumeric participant ID (1–17; consistent with Player_ID order)
D: WeekTesting week number (1–36; week 28 absent)
E: MDMatch day descriptor (e.g., PS = pre-season, MD+2 = 2 days post-match)
F: MD_GroupMatch day group code (numeric; 0 = pre-season)
G: MassBody mass (kg)
H: HeightHeight (m)
I: PositionPlaying position (GK, Def, Mid, Centre-Forward)
J: Player_GroupPosition group label (GK, Def, Mid, Fwd)
K: GroupPosition group code (1 = GK, 2 = Def, 3 = Mid, 4 = Fwd)
L: Add_NonDomHip adduction peak force — non-dominant limb (N)
M: Add_DomHip adduction peak force — dominant limb (N)
N: Abd_NonDomHip abduction peak force — non-dominant limb (N)
O: Abd_DomHip abduction peak force — dominant limb (N)
P: DomVsNonDom_AddAdduction limb symmetry ratio (Dom/NonDom)
Q: DomVsNonDom_AbdAbduction limb symmetry ratio (Dom/NonDom)
R: Dom_AddAbdDominant limb adduction:abduction ratio
S: NonDom_AddAbdNon-dominant limb adduction:abduction ratio
T: Norm_Add_NonDomAdduction NonDom normalised to body mass (N·kg⁻¹)
U: Norm_Add_DomAdduction Dom normalised to body mass (N·kg⁻¹)
V: Norm_Abd_NonDomAbduction NonDom normalised to body mass (N·kg⁻¹)
W: Norm_Abd_DomAbduction Dom normalised to body mass (N·kg⁻¹)

The dominant limb was defined as the preferred kicking limb, determined through player self-report and confirmed by coaching staff.

Missing Data
Blank cells indicate sessions where a player did not complete testing (e.g., injury absence, non-attendance). Missing values are represented as empty cells (NaN in MATLAB). The analysis script handles missing data using omitnan flags throughout.

Code Description
SevenFormulas_MasterScript.m
A single self-contained MATLAB script that reproduces all analyses, figures, and statistics reported in the paper.

Requirements:
MATLAB R2021b or later
Statistics and Machine Learning Toolbox
Data_SeasonLong_Anonymous.xlsx placed in the same folder as the script

No path editing is required. The script uses fileparts(mfilename('fullpath')) to locate the data file relative to its own location.
To run: open SevenFormulas_MasterScript.m in MATLAB and press Run (or type SevenFormulas_MasterScript in the Command Window).

Outputs:
OutputType
Figures 1–10MATLAB figures
All statistics reported in ResultsPaperB_ThresholdSensitivity.xlsx
Flagging rates across 0–25% thresholds
PaperB_Stabilisation.xlsxExcelPer-player stabilisation week by formula
PaperB_CI_HalfWidths.xlsxExcel95% CI half-widths at 4, 8, 12, 20 observations

Section index within the script:
1. Load data
2. Compute seven asymmetry formulas
3. Descriptive statistics (Table 1)
4. Clinical flagging rates (10% and 15%)
5. Inter-formula Pearson correlations (Figure 2)
6. Bland-Altman analysis: |BSA| vs |BAI-2| (Figure 4)
7. Threshold sensitivity analysis (Figure 5)
8. Direction-of-asymmetry consistency and weekly kappa
9. Rolling direction consistency (Figure 6)
10. Season-long spaghetti plots: BSA vs BAI-2 (Figure 7)
11. Weekly asymmetry by formula (Figure 1)
12. Per-player flagging heatmap (Figure 3)
13. Individual vs position-group representativeness (Figures 8 & 9)
14. Per-player cumulative-mean stabilisation (Figure 10)
15. 95% CI half-width analysis
16. Abduction and ratio supplementary results
17. Helper functions