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Short-term performance prediction in team sports using tracking data Cover

Short-term performance prediction in team sports using tracking data

Open Access
|Aug 2026

Figures & Tables

Table 1:

Games locations and outcomes

WinDrawLossTotal
Away4-1317
Home242430
Total2821747
Table 2:

Number of actions and sequences per time horizon

Time HorizonActionsSequences
16-161,332112,325
32-321,09390,033
64-6479557,629
128-12830323,771
Fig. 1:

Illustration of 4 randomly selected target series P over different time horizons. The vertical dashed gray line represents the separation between known sequence on the left and prediction sequence on the right, the horizontal dotted black line represents the Stand Still baseline and the colored dotted lines the Constant Velocity baseline associated with the series displayed with the same color.

Fig. 2:

MSE and best epoch of the 5 best experiments for each model over the validation set.

Fig. 3:

Distribution of FDE relative improvement (in %) vs best baseline (Linear Regression for all cases) for Multivariate (M) and Univariate (U) inputs models for each time horizon. Each graph represents the results of the test procedure over the corresponding time horizon. The number in parenthesis indicates the number of models tested (see 4.1) and the model names located below the boxplots indicate the models reaching the lowest minimum FDE over the test set. The dashed red line at y = 0 materializes the score of the best performing baseline. Results from Mann-Whitney U tests are reported above the boxplots. Significance code: * p<0.05, ** p< 0.01, *** p<0.001, **** p<0.0001.

Fig. 4:

Analysis of Permutation importance for time horizon 128-128. The left panel displays the importance per feature type (position over the x-axis and y-axis, velocity and goal angle). The dashed red line materializes the 0-impact level while the dotted green line the importance of the performance feature. The middle panel indicates the importance of features per player. The heatmap of the right panel indicates feature importance over the two combined dimensions. The black line separates the forwards (player 1 to 8) and backs (players 9 to 15).

Table 1:

Testing summary - Time horizon 16-16

ModelInputsnMin. FDEμσ
CrossformerMultivariate123.903.94±0.03
CrossformerUnivariate73.953.96±0.01
PyraformerMultivariate94.114.13±0.01
StandStillBaseline15.235.23-
Constant VelocityBaseline16.136.13-
Linear RegressionBaseline14.504.50-
Table 2:

Testing summary - Time horizon 32-32

ModelInputsnMin. FDEμσ
CrossformerMultivariate576.606.71±0.05
CrossformerUnivariate66.866.88±0.02
Non Stationary TransformerUnivariate67.468.49±0.61
PyraformerMultivariate137.047.10±0.03
PyraformerUnivariate36.916.93±0.02
iTransformerMultivariate56.936.96±0.02
StandStillBaseline18.448.44-
Constant VelocityBaseline111.4111.41-
Linear RegressionBaseline17.827.82-
Table 3:

Testing summary - Time horizon 64-64

ModelInputsnMin. FDEμσ
CrossformerMultivariate409.8310.13±0.13
CrossformerUnivariate110.4810.48-
PatchTSTMultivariate1510.0110.13±0.07
PyraformerMultivariate210.2010.22±0.03
PyraformerUnivariate1810.4310.57±0.07
iTransformerMultivariate149.859.95±0.06
StandStillBaseline111.7911.79-
Constant VelocityBaseline117.5617.56-
Lineal RegressionBaseline111.1411.14-
Table 4:

Testing summary - Time horizon 128-128

ModelInputsnMin. FDEμσ
CrossformerMultivariate1813.4813.68±0.11
CrossformerUnivariate313.6813.78±0.09
DLinearMultivariate213.6513.72±0.10
LSTMMultivariate713.9314.35±0.28
PatchTSTMultivariate8812.2913.07±0.44
PyraformerMultivariate713.5113.99±0.31
PyraformerUnivariate3013.5913.94±0.25
TransformerMultivariate513.7013.71±0.01
TransformerUnivariate414.3814.68±0.27
iTransformerMultivariate7112.7413.50±0.35
StandStillBaseline115.0915.09-
Constant VelocityBaseline125.1725.17-
Linear RegressionBaseline113.4613.46-
Language: English
Page range: 50 - 67
Published on: Aug 7, 2026
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services

© 2026 Arnaud Odet, Thomas Bechard, Sebastien Déjean, Cristian Pasquaretta, published by International Association of Computer Science in Sport
This work is licensed under the Creative Commons Attribution 4.0 License.