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Real Time Quantification of Dangerousity in Football Using Spatiotemporal Tracking Data


  • Daniel Link
  • Steffen Lang
  • Philipp Seidenschwarz


This study describes an approach to quantification of attacking performance in football. Our procedure determines a quantitative representation of the probability of a goal being scored for every point in time at which a player is in possession of the ball–we refer to this as dangerousity. The calculation is based on the spatial constellation of the player and the ball, and comprises four components: (1) Zone describes the danger of a goal being scored from the position of the player on the ball, (2) Control stands for the extent to which the player can implement his tactical intention on the basis of the ball dynamics, (3) Pressure represents the possibility that the defending team prevent the player from completing an action with the ball and (4) Density is the chance of being able to defend the ball after the action. Other metrics can be derived from dangerousity by means of which questions relating to analysis of the play can be answered. Action Value represents the extent to which the player can make a situation more dangerous through his possession of the ball. Performance quantifies the number and quality of the attacks by a team over a period of time, while Dominance describes the difference in performance between teams. The evaluation uses the correlation between probability of winning the match (derived from betting odds) and performance indicators, and indicates that among Goal difference (r = .55), difference in Shots on Goal (r = .58), difference in Passing Accuracy (r = .56), Tackling Rate (r = .24) Ball Possession (r = .71) and Dominance (r = .82), the latter makes the largest contribution to explaining the skill of teams. We use these metrics to analyse individual actions in a match, to describe passages of play, and to characterise the performance and efficiency of teams over the season. For future studies, they provide a criterion that does not depend on chance or results to investigate the influence of central events in a match, various playing systems or tactical group concepts on success.

Suggested Citation

  • Daniel Link & Steffen Lang & Philipp Seidenschwarz, 2016. "Real Time Quantification of Dangerousity in Football Using Spatiotemporal Tracking Data," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-16, December.
  • Handle: RePEc:plo:pone00:0168768
    DOI: 10.1371/journal.pone.0168768

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    Cited by:

    1. Daniel Linke & Daniel Link & Martin Lames, 2020. "Football-specific validity of TRACAB’s optical video tracking systems," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-17, March.
    2. Stijn Baert & Simon Amez, 2018. "No better moment to score a goal than just before half time? A soccer myth statistically tested," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    3. Bruno Gonçalves & Diogo Coutinho & Juliana Exel & Bruno Travassos & Carlos Lago & Jaime Sampaio, 2019. "Extracting spatial-temporal features that describe a team match demands when considering the effects of the quality of opposition in elite football," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-20, August.
    4. Yurko Ronald & Matano Francesca & Richardson Lee F. & Pospisil Taylor & Ventura Samuel L. & Granered Nicholas & Pelechrinis Konstantinos, 2020. "Going deep: models for continuous-time within-play valuation of game outcomes in American football with tracking data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(2), pages 163-182, June.

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