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Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science.

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Summary
This summary is machine-generated.

Targeted Maximum Likelihood Estimation (TMLE) offers a robust approach to causal inference in sports science, outperforming traditional methods like Generalized Linear Models (GLM). This method accurately estimates the effect of substitutions on soccer team performance, even with imperfect data.

Keywords:
TMLEcausal inferencemachine learningmethodsstatistics

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Area of Science:

  • Sports Science
  • Causal Inference
  • Statistical Modeling

Background:

  • Causal inference is valuable in various fields but underutilized in sports science.
  • Targeted Maximum Likelihood Estimation (TMLE) is an advanced causal inference method known for its robustness to model misspecification and integration with machine learning.
  • Generalized Linear Models (GLM) are commonly used but can be sensitive to model assumptions.

Purpose of the Study:

  • To introduce TMLE and a roadmap for its application in sports science.
  • To compare the performance of TMLE against GLM for estimating effect sizes in sports science.
  • To investigate the influence of player substitutions on the total physical performance of soccer teams using causal inference.

Main Methods:

  • Development of a causal model and a misspecified causal model.
  • Utilizing simulation datasets and an observed tracking dataset from 302 elite soccer matches.
  • Comparison of effect size estimation between TMLE and GLM.

Main Results:

  • TMLE demonstrated superior performance over GLM in estimating the effect size of substitutions on total physical performance in simulation data.
  • TMLE exhibited greater robustness against model misspecification in both simulation and real-world tracking datasets.
  • Analysis of the tracking dataset indicated that substitutions positively impact the overall physical performance of soccer teams, irrespective of the statistical method used.

Conclusions:

  • TMLE is a valuable and robust tool for causal inference in sports science, particularly for estimating the impact of interventions like substitutions.
  • The study provides a practical framework for applying TMLE in sports science research.
  • Substitutions are confirmed to enhance the collective physical performance of soccer teams.