Outplayed Opponents (OPO)

Category: Passing
Level: Player Report

What: Average number of outplayed opponents over all forward successful passes. 

Why: Indicator for the offending values of the passes in the match. 

Interpretation: The higher this value the more players were outplayed per pass. Passes that outplay more players often bring the team in an offensive advantage. 

Example 1:

Figure 1. Schematic utplaying three players by a pass from P1 to P2

Situation 1 (before pass): eight players (keeper excluded) between the ball and the goal at the x-axis.
Situation 2 (after receiving): only five players left between the ball and the goal.

The difference between situation 1 and situation 2 is that there are two opposing players outplayed.  Therefore, the pass performed by player P1 outplayed 3 players.

Example 2:

Figure 2. Outplaying three players by a pass from P1 to P2

Situation 1 (before pass): The orange arrows point at the players between P1 (passer) and P2 (receiver of the pass). For this feature only the x-coordinates (length of field) are considered.
Situation 2 (after receiving): The red arrows point at the players that are outplayed by the pass.

Argumentation for Outplayed Opponents Ratio

There are numerous models [2][3][5] and calculations available to quantify passes and their 'efficiency', however, there are a lot of factors when considering the effectiveness of a pass. The more accurate the model the more complex the calculation will be. Some models try to grasp the complexity of the game or a pass in a single all-encompassing value [2]. This drawback to complex calculations is that they are hard to translate back to the context of a football match. For this reason we decided to include a straight forward measure of outplayed opponents in our passing analysis. By displaying this understandable and objective measure we allow the end-user to use the match reports complimentary to his/her vision on the game. 

If you have a question or suggestion regarding this feature, we are more than happy to hear from you. 

Related features:

  • pass clasification

  • pass efficiency

  • dangerousity

  • risk-reward model


1. Memmert & Rein (2018)  Match Analysis, Big Data and Tactics: Current Trends in Elite Soccer

2. Robert Reina, Dominik Raabe, Daniel Memmerta (2017) “Which pass is better?” Novel approaches to assess passing effectiveness in elite soccer 

3. Goes, Kempe, Meerhoff (2018) Not Every Pass Can Be an Assist: A Data-Driven Model to Measure Pass Effectiveness in Professional Soccer Matches 

4. Link D, Lang S, Seidenschwarz P. Real time quantification of dangerousity in football using spatiotemporal tracking data. PLoS One. 2016;11: e0168768.

5. Power P, Ruiz H, Wei X, Lucey P. (2017) Not all passes are created equal. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD’17. 

6. Chawla S, Estephan J, Horton M, Gudmundsson J. (2017) Classification of passes in football matches using spatiotemporal data. ACM Trans Spatial Algorithms Syst.

7. Frencken W, De Poel H, Visscher C, Lemmink K. Variability of inter-team distances associated with match events in elite-standard soccer. J Sports Sci. 2012;30:1207–1213.


Packing: https://www.youtube.com/watch?v=fk8yAQU9U1E (video) packing with examples : http://bundesligafanatic.com/20160610/impect-packing-the-future-of-football-analytics-is-here/

Interview inventor packing: https://www.facebook.com/RWTHAachenUniversity/videos/10154918639018448/





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