cs.LGMar 30, 2026
SaveStructural Pass Analysis in Football: Learning Pass Archetypes and Tactical Impact from Spatio-Temporal Tracking Data
Organizations: Cardiff University, School of Computer Science and Informatics, Abacws, Senghennydd Road, Cardiff, CF24 4AG, UK · Dead Ball Analytics Limited, Barry, CF63 2QQ, UK
Abstract
Passing actions are commonly evaluated through ball progression and attacking outcomes, yet their relationship with the spatial organisation of the opposition provides an additional perspective on passing behaviour. This study introduces an interpretable framework for describing passing structure using synchronised event and tracking data. Three complementary descriptors are proposed: Line Bypass Score (LBS), measuring the difference in goal-side opponent counts between passer and receiver; Space Gain Metric (SGM), comparing their nearest-opponent distances; and Structural Disruption Index (SDI), summarising differences in defensive organisation between pass initiation and a sampled state one second later. Following transformation and standardisation, these descriptors are combined into Tactical Impact Value (TIV), a fixed-weight structural composite. Applied to 41,078 open-play passing events from the 2022 FIFA World Cup, the framework identifies four interpretable profiles: Circulatory, Destabilising, Line-breaking, and Space-expanding. The profiles exhibit distinct structural signatures and different distributions of territorial-crossing and recorded attacking indicators. Team, spatial, player, and passer--receiver analyses further demonstrate how the representation can describe passing behaviour across analytical scales. The framework complements outcome-oriented approaches by connecting individual passing actions with measurable characteristics of opposition positioning and defensive organisation.
Figures & tables
| Quantity | Value |
|---|---|
| Matches | 64 |
| Teams | 32 |
| Total analysed passes | 41,078 |
| Mean passes per match | 641.84 |
| Tracking frame rate | 29.97 |
| Players tracked per frame | 22 |
Table 1: Summary of the dataset used for structural pass analysis.
Figure 1: Analytical workflow integrating event and tracking data, structural descriptors, TIV, pass clustering, and subsequent team- and player-level analyses.
Figure 2: Conceptual illustration of the four structural passing profiles: Circulatory, Destabilising, Line-breaking, and Space-expanding.
Figure 3: Illustration of the structural descriptors. LBS compares goal-side opponent counts at passer and receiver coordinates within the same pass-time configuration; SGM compares nearest-opponent distances. SDI measures differences between the pass-time defensive configuration and the nearest sampled state approximately one second later, rather than an identified reception state.
| Metric | Definition | Tactical interpretation |
|---|---|---|
| Line Bypass Score ( LBS ) | Signed difference between pass-time goal-side opponent counts at the tracked passer and receiver x-coordinates. | Positive values indicate fewer opponents goal-side at the receiver coordinate; negative values are possible. |
| Space Gain Metric ( SGM ) | Ratio of receiver to floored passer nearest-opponent distance, using the same pass-time opponent snapshot. | Values above 1 indicate a larger receiver nearest-opponent distance relative to the floored passer distance. |
| Structural Disruption Index ( SDI ) | Unnormalised sum of absolute changes in defensive width, depth, centroid x/y, and convex-hull area between pass-time and one-second-later sampled states. | Higher values indicate larger aggregate differences across the five shape summaries; the total does not indicate change direction. |
Table 2: Definitions and interpretations of the three structural passing descriptors.
Figure 4: Representative examples of the four structural pass archetypes. Each panel shows a pass in tracking context together with the surrounding defensive structure.
Figure 5: Three-dimensional scatterplot of pass events using the raw Line Bypass Score, Space Gain Metric, and Structural Disruption Index values. Colours indicate assignment to the selected operational pass archetype; the plotted coordinates are not the transformed and standardised features used to fit K-means.
| Pass type | Line Bypass Score | Space Gain Metric | Structural Disruption Index |
|---|---|---|---|
| Circulatory | 0.10 | 0.97 | 0.26 |
| Destabilising | -1.36 | 1.11 | 43.06 |
| Line-breaking | 4.49 | 0.93 | 33.68 |
| Space-expanding | -1.55 | 4.96 | 16.87 |
Table 3: Raw arithmetic member means of LBS, SGM, and SDI by operational pass archetype. Values are calculated over assigned pass events and are neither standardised feature means nor K-means centroid coordinates; clustering used transformed and standardised features.
Figure 6: Two-dimensional PCA projection of the three raw structural-metric columns. The plotted component coordinates are descriptive projection scores, not separately measured tactical dimensions; the four operational profiles occupy distinguishable but partially overlapping regions.
| Pass type | Count | Percentage |
|---|---|---|
| Circulatory | 14144 | 34.43% |
| Destabilising | 12836 | 31.25% |
| Line-breaking | 7507 | 18.28% |
| Space-expanding | 6591 | 16.04% |
Table 4: Distribution of structural pass archetypes.
Figure 7: Observed within-archetype rates for direct pass-coordinate final-third and box-entry crossings and for the stored binary shot and goal outcome flags. Territorial crossings describe the pass itself, while the shot and goal columns summarise the corresponding stored indicators.
| Pass type | Final-third crossing | Box-entry crossing | Stored shot flag | Stored goal flag |
|---|---|---|---|---|
| Circulatory | 0.043976 | 0.014777 | 0.048925 | 0.006717 |
| Destabilising | 0.055235 | 0.022671 | 0.034668 | 0.005142 |
| Line-breaking | 0.121220 | 0.030372 | 0.052218 | 0.007326 |
| Space-expanding | 0.120164 | 0.050827 | 0.035200 | 0.004096 |
Table 5: Within-archetype rates for direct pass-coordinate territorial-crossing indicators and stored binary shot and goal flags. Final-third and box entries are defined by coordinate-crossing criteria, while shot and goal rates are calculated from the corresponding stored binary variables.
Figure 8: Two-dimensional PCA-based display of team profiles. The axis, quadrant, and colour annotations provide interpretive descriptions of regions within the projection rather than directly measured tactical dimensions or independently verified dominant-archetype classifications.
Figure 9: PCA-based team positions with the within-team mean stored shot flag represented by bubble size and the box-entry crossing rate represented by colour. The display combines team-level structural profiles with descriptive attacking indicators.
Figure 10: Distribution of Tactical Impact Value ( TIV ) across structural pass archetypes.
| Pass type | Mean TIV | Median TIV | Std. Dev. |
|---|---|---|---|
| Circulatory | -0.514069 | -0.521473 | 0.308296 |
| Destabilising | 0.124631 | 0.146360 | 0.349846 |
| Line-breaking | 0.588821 | 0.576782 | 0.360720 |
| Space-expanding | 0.189798 | 0.153157 | 0.538823 |
Table 6: Summary statistics of Tactical Impact Value ( TIV ) by structural pass type.
Figure 11: Observed rates across Tactical Impact Value quantiles for direct pass-coordinate final-third and box-entry crossings and the stored binary shot flag. The territorial indicators describe coordinate crossings, while the shot series represents the corresponding stored binary variable.
Figure 12: Spatial heatmaps of mean Tactical Impact Value by pass location. Panel (a) shows the mean TIV of passes by origin bin, while Panel (b) shows the corresponding mean by destination bin. The maps summarise the coordinate fields used in their construction; their direction-normalisation state is not uniquely established.
Figure 13: Team-level maps of mean destination Tactical Impact Value for eight selected World Cup teams. Colour represents the mean structural-composite value within each displayed coordinate bin. The exact direction-normalisation state of the mapped coordinates is not uniquely established.
Figure 14: Player-level structural passing profiles for the 15 displayed players ranked by cumulative Tactical Impact Value (TIV), after applying a minimum threshold of 40 mean analysed passes per match. “Passes (Avg)” is the mean pass count per match. The table reports mean structural metrics, cumulative and mean TIV, and the proportions of passes in each operational archetype. Cumulative TIV reflects both pass-level values and the number of analysed passes, whereas mean TIV is the average per analysed pass.
Figure 15: The 10 displayed passer–receiver pairs with the largest among pairs from the 10 passers with the highest cumulative TIV, with a minimum of 10 passes per pair. is the difference between pair mean TIV and the passer’s mean TIV across all analysed passes. Shot and goal columns report within-pair proportions of stored binary flags, while territorial entries represent the corresponding stored-coordinate crossing indicators.