Predicting match outcome in professional Dutch football using tactical performance metrics computed from position tracking data

EasyChair Preprint 993

Abstract

Quality as well as quantity of tracking data have rapidly increased over the recent years, and multiple leagues have programs for league-wide collection of tracking data. Tracking data enables in-depth performance analysis, especially with regard to tactics. This already resulted in the development of several Key Performance Indicators (KPI’s) related to scoring opportunities, outplaying defenders, numerical balance and territorial advantage. Although some of these KPI’s have gained popularity in the analytics community, little research has been conducted to support the link with performance. Therefore, we aim to study the relationship between match outcome and tactical KPI’s derived from tracking data. Our dataset contains tracking data of all players and the ball, and match outcome, for 118 Dutch premier league matches. Using tracking data, we identified 72.989 passes. For every pass-reception window we computed KPI’s related to numerical superiority, outplayed defenders, territorial gains and scoring opportunities using position data. This individual data was then aggregated over a full match. We then split the dataset in a train and test set, and predicted match outcome using different combinations of features in a logistic regression model. KPI’s related to a combination of off-the-ball features seemed to be the best predictor of match outcome (accuracy of 64.0% and a log loss of 0.67), followed by KPI’s related to the creation of scoring opportunities (accuracy of 58% and a log loss of 0.69). This indicates that although most (commercially) available KPI’s are based on ball-events, the most important information seems to be in off-the-ball activity. We have demonstrated that tactical KPI’s computed from tracking data are relatively good predictors of match outcome. As off-the-ball activity seems to be the main predictor of match outcome, tracking data seems to provide much more insight than notational analysis.

Keyphrases: Soccer, Tactical behaviour, performance analysis, spatiotemporal analysis, tracking data

@booklet{EasyChair:993,
  author    = {Floris Goes and Matthias Kempe and Koen Lemmink},
  title     = {Predicting match outcome in professional Dutch football using tactical performance metrics computed from position tracking data},
  doi       = {10.29007/4jjb},
  howpublished = {EasyChair Preprint 993},
  year      = {EasyChair, 2019}}