Evidence map›Paper›PMID 40462142›Full record

ArticleBMC sports science, medicine & rehabilitation2025

Performance assessment in elite table tennis matches using the enhanced first offensive shot model.

Ruizhi Liu, Frederic Rothe, Martin Lames

Abstract read
In one paragraph

Article in BMC sports science, medicine & rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
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4citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ruizhi LiuChina Table Tennis College, Shanghai University of Sport, 650 Qingyuanhuan Road, Shanghai, 200438, China. liuruizhi_2014@163.com.
Frederic RotheSchool of Medicine and Health, Technical University of Munich, Georg-Brauchle-Ring 60/62, 80992, Munich, Germany.
Martin LamesSchool of Medicine and Health, Technical University of Munich, Georg-Brauchle-Ring 60/62, 80992, Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTechnique and tactics are key elements in assessing table tennis performance and have been widely studied. However, most existing methods classify shots solely by sequence or shot number, overlooking the tactical attributes and their impact on rally outcomes. The First Offensive Shot (FOS) model attempted to address this issue but remained too coarse-grained. To overcome these limitations, this study introduces the Enhanced FOS model (EFOS), which integrates shot type and shot number into a more refined state-transition framework. This novel approach provides a more detailed representation of rally dynamics, enabling a deeper analysis of rally progression.

methodsThe EFOS model divides a rally into three phases: the pre-offensive phase (POP), the initial-offensive phase (IOP), and the final-offensive phase (FOP). Each phase is further categorised based on shot type and shot number. Transition probabilities between states were computed. A comprehensive analysis was conducted on 105 men's and women's singles matches from the Olympic Games Tokyo 2020, with gender, ranking, and match results as independent variables.

resultsThe IOP accounts for the highest usage (70.6%) among the three phases, with FOS and the following shot-FOS + 1-rank second. Male players tend to use defensive techniques to return shots and attack later, whereas female players prefer to attack defensive shots directly. High-ranked players (≤ 40) demonstrated superior transition efficiency and lower error rates, particularly in the IOP, while winners consistently exhibited higher transition rates and fewer errors across all phases.

conclusionsFirstly, the initial-offensive phase emerges as pivotal, with the first offensive shot and the subsequent shot significantly influencing rally outcomes. Secondly, attacking defensive shots have proven to be more effective than attacking serves. Thirdly, male players outperform female players in both initial and subsequent attack. These findings offer practical insights for players and coaches, suggesting that training should emphasise the initial-offensive phase and focus on reducing errors during transitions, particularly for low-ranked players. Additionally, gender-specific training strategies may be developed to address the observed performance differences.

Indexed as

Athletic performanceGender differenceObservational analysisOlympic Games Tokyo 2020Racket sportsState transition

Identifiers

PMID40462142
PMCPMC12131581

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.