pith. sign in

arxiv: 2606.21009 · v1 · pith:OV7VNPIEnew · submitted 2026-06-19 · 📊 stat.OT

Movement synchronization in complex and dynamic team coordination: Relationship with mutual anticipation

Jun Ichikawa , Mizuki Yokoyama , Soma Ishida , Genki Ichinose This is my paper

Pith reviewed 2026-06-26 12:56 UTC · model grok-4.3

classification 📊 stat.OT
keywords movement synchronizationmutual anticipationteam coordinationbasketballrelative phaseBayesian inferencecoaching adviceoffensive coordination
0
0 comments X

The pith

Coaching advice on offensive strategy raises the probability of synchronized movements among 3-on-3 basketball players to 70 percent or higher.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that mutual anticipation among teammates produces measurable synchronization in their movements during fast-paced competition. In a field experiment, players took part in 3-on-3 mini-games; researchers tracked the relative phases of movements toward or away from teammates and used Bayesian inference to compare the frequency distributions of those phases before and after advice on offensive coordination. The analysis found that the probability of a synchronization trend after the advice reached 70 percent or more. A sympathetic reader would care because the work offers a statistical way to detect when implicit team interactions become more stable without relying on verbal signals. The result points to coaching as a practical lever for strengthening anticipation in roles that change over time.

Core claim

Relative phases were extracted from the movements of offensive players in 3-on-3 basketball mini-games. Frequency distributions of these phases were estimated with Bayesian methods and compared before versus after advice on team offensive coordination. The probability that a synchronization trend appeared after advice, relative to before, reached 70 percent or higher. The authors interpret this shift as a signature of mutual anticipation grounded in the shared strategy conveyed through coaching.

What carries the argument

Relative phase calculation between pairs of offensive players, combined with Bayesian estimation of phase-frequency distributions to detect changes in temporal stability before and after advice.

If this is right

  • Synchronization marks a temporally stable pattern of interaction that allows teammates to act without waiting for visible cues.
  • Mutual anticipation reduces gaps that opponents can exploit in direct-competition settings.
  • Advice that establishes a common team strategy can measurably increase the likelihood of this stable pattern.
  • Bayesian comparison of phase distributions supplies a probabilistic test for coordination changes in evolving, heterogeneous teams.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same phase-distribution method could be applied to non-sport team tasks where roles shift rapidly, such as emergency medical teams or small-unit military maneuvers.
  • If synchronization reliably precedes better scoring or defensive outcomes, coaches could monitor phase trends as an in-game feedback signal.
  • Controlled experiments that hold opponent pressure constant would help isolate whether the observed synchronization gain is specific to anticipation or partly reflects general arousal from the coaching session.

Load-bearing premise

Observed shifts in the relative-phase distributions are driven mainly by changes in mutual anticipation rather than by opponent behavior, fatigue, or other game-specific factors.

What would settle it

A follow-up trial in which the same advice is delivered yet the post-advice synchronization probability remains at or below the pre-advice baseline under matched opponent and fatigue conditions.

Figures

Figures reproduced from arXiv: 2606.21009 by Genki Ichinose, Jun Ichikawa, Mizuki Yokoyama, Soma Ishida.

Figure 2
Figure 2. Figure 2: FIGURE 2 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 1
Figure 1. Figure 1: FIGURE 1 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIGURE 3 [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIGURE 4 [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIGURE 5 [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
read the original abstract

Mutual anticipation of teammates' actions enables efficient interactions in team coordination that achieves a common goal and high performance. In team sports involving direct competition, such implicit and non-verbal interactions within short periods are required. If players begin moving only after observing their teammates, gaps may emerge, allowing opponents to interfere. When mutual anticipation functions properly, players' interactions are smooth without gaps, and their movements are expected to become synchronized. Synchronization represents a temporally stable structure in interactions and its mechanisms have been examined in previous studies. However, few studies have investigated synchronization in real-world coordination involving heterogeneous roles and interactions evolving over time, or quantitatively examined how temporally stable structures differ from a baseline. In our approach, we utilized team sports and introduced a statistical method to probabilistically examine these differences. The purpose of this study was to extract the temporal components of movement using 3-on-3 basketball. We calculated the relative phases in which players approached or moved away from their teammates during mini-games in a field experiment that investigated the effects of advice on offensive coordination. These frequency distributions were estimated using Bayesian inference and were compared before and after advice. The results showed that the probability of a synchronization trend among the offensive players after advice compared with before advice reached 70\% or higher. This may be a typical case that is related to mutual anticipation based on the team strategy established through coaching. These findings contribute to a quantitative understanding of coordination processes.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript claims that in a 3-on-3 basketball field experiment, Bayesian estimation of relative-phase frequency distributions (players approaching or moving away from teammates) yields a posterior probability of 70% or higher for a synchronization trend among offensive players after coaching advice on offensive coordination, compared with before advice; this is interpreted as evidence that the change reflects increased mutual anticipation arising from an established team strategy.

Significance. If the reported probability can be shown to isolate the effect of advice after accounting for game-level factors, the work supplies a probabilistic method for detecting shifts in temporal stability of interpersonal movement in heterogeneous, competitive team settings. The Bayesian treatment of phase distributions is a methodological strength for handling noisy, short-duration observations typical of real-world coordination.

major comments (2)
  1. [Abstract] Abstract and implied Methods: the before-versus-after comparison of marginal relative-phase frequency distributions does not incorporate game-level covariates (opponent identity, score/time context, or fatigue proxies), nor a no-advice control arm; because the design is within-subject and competitive, any systematic shift in defender positioning or mini-game context across the advice window can produce the observed change in phase locking without requiring mutual anticipation.
  2. [Abstract] Abstract/Results: no sample size, number of mini-games, number of offensive triads, prior specifications, or posterior credible intervals are reported for the Bayesian model; without these quantities the claim that P(synchronization trend after advice) ≥ 70% cannot be evaluated for robustness or sensitivity to modeling choices.
minor comments (1)
  1. The abstract refers to 'mini-games in a field experiment' but supplies no description of trial structure, randomization of advice, or how relative phases were extracted from position time series.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive review of our manuscript on movement synchronization in 3-on-3 basketball. We address each major comment point by point below, providing the strongest honest defense and indicating where revisions will be incorporated.

read point-by-point responses

  1. Referee: [Abstract] Abstract and implied Methods: the before-versus-after comparison of marginal relative-phase frequency distributions does not incorporate game-level covariates (opponent identity, score/time context, or fatigue proxies), nor a no-advice control arm; because the design is within-subject and competitive, any systematic shift in defender positioning or mini-game context across the advice window can produce the observed change in phase locking without requiring mutual anticipation.

    Authors: We agree that the analysis relies on marginal before-after comparisons without explicit game-level covariates or a control arm, which is a genuine limitation of the within-subject field design. This leaves open the possibility that changes in defensive positioning or other contextual factors could contribute to the observed shift in phase distributions. A no-advice control arm was not included in the original experimental protocol, so we cannot add one. In revision we will expand the Discussion to explicitly address these potential confounds and their implications for attributing the change to mutual anticipation. We will also examine whether any recorded contextual variables (e.g., score or elapsed time) can be added as covariates in a supplementary analysis. revision: partial

  2. Referee: [Abstract] Abstract/Results: no sample size, number of mini-games, number of offensive triads, prior specifications, or posterior credible intervals are reported for the Bayesian model; without these quantities the claim that P(synchronization trend after advice) ≥ 70% cannot be evaluated for robustness or sensitivity to modeling choices.

    Authors: The full manuscript reports the sample size, number of mini-games, number of offensive triads, prior specifications, and posterior credible intervals in the Methods and Results sections. We will revise the abstract to include these quantitative details (e.g., observation counts and the exact posterior probability with credible intervals) so that readers can directly assess robustness and sensitivity. revision: yes

Circularity Check

0 steps flagged

No circularity: standard Bayesian comparison of observed phase distributions

full rationale

The paper applies Bayesian inference directly to empirical relative-phase frequency distributions collected before versus after advice in the 3-on-3 experiment. The reported posterior probability (≥70%) that a synchronization trend exists after advice is the direct numerical output of that comparison on the observed data; it is not obtained by fitting a parameter to the target quantity, by self-definition of the synchronization metric, or by any self-citation chain that imports the result. No equations or derivations in the described method reduce the central claim to its own inputs by construction. The analysis therefore remains self-contained against external benchmarks of Bayesian model comparison on time-series phase data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on abstract; full model details unavailable. The interpretation assumes relative phase distributions directly index mutual anticipation.

axioms (1)
  • domain assumption Relative phase distributions capture temporally stable synchronization structures arising from mutual anticipation in team coordination.
    Invoked in the interpretation linking post-advice probability increase to mutual anticipation.

pith-pipeline@v0.9.1-grok · 5792 in / 1216 out tokens · 16947 ms · 2026-06-26T12:56:09.081018+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

32 extracted references · 30 canonical work pages

  1. [1]

    R. L. Goldstone, E. J. Andrade-Lotero, R. D. Hawkins, and M. E. Roberts, ‘‘The emergence of specialized roles within groups,’’Top. Cogn. Sci., vol. 16, no. 2, pp. 257–281, Apr. 2024, 10.1111/tops.12644

    work page doi:10.1111/tops.12644 2024

  2. [2]

    Knoblich, S

    G. Knoblich, S. Butterfill, and N. Sebanz, ‘‘Psychological research on joint action: Theory and data,’’ inThe Psychology of Learning and Motivation: Advances in Research and Theory, B. Ross, Ed. Academic Press, 2011, pp. 59–101

    2011

  3. [3]

    Y okoyama, H

    K. Y okoyama, H. Shima, K. Fujii, N. Tabuchi, and Y . Y amamoto, ‘‘Social forces for team coordination in ball possession game,’’Phys. Rev. E, vol. 97, Art. no. 022410, Feb. 2018, 10.1103/PhysRevE.97.022410

    work page doi:10.1103/physreve.97.022410 2018

  4. [4]

    J. C. Gorman, ‘‘Team coordination and dynamics: Two central issues,’’ Curr . Dir . Psychol. Sci., vol. 23, no. 5, pp. 355–360, Oct. 2014

    2014

  5. [5]

    Steiner, A

    S. Steiner, A. C. Macquet, and R. Seiler, ‘‘An integrative perspective on interpersonal coordination in interactive team sports,’’Front. Psychol., vol. 8, Art. no. 1440, Aug. 2017, 10.3389/fpsyg.2017.01440

    work page doi:10.3389/fpsyg.2017.01440 2017

  6. [6]

    Farrow and B

    D. Farrow and B. Abernethy, ‘‘Do expertise and the degree of perception– action coupling affect natural anticipatory performance?,’’Perception, vol. 32, no. 9, pp. 1127–1139, Oct. 2003, doi: 10.1068/p3323

    work page doi:10.1068/p3323 2003

  7. [7]

    Mori and T

    S. Mori and T. Shimada, ‘‘Expert anticipation from deceptive action,’’ Atten. Percept. Psychophys., vol. 75, no. 4, pp. 751–770, Feb. 2013, 10.3758/s13414-013-0435-z

    work page doi:10.3758/s13414-013-0435-z 2013

  8. [8]

    Müller, K

    S. Müller, K. Morris-Binelli, D. Z. Hambrick, and B. N. Macnamara, ‘‘Accelerating visual anticipation in sport through temporal occlusion train- ing: A meta-analysis,’’Sports Med., vol. 54, pp. 2597–2606, Aug. 2024, 10.1007/s40279-024-02073-6

    work page doi:10.1007/s40279-024-02073-6 2024

  9. [9]

    Shimizu and T

    D. Shimizu and T. Okada, ‘‘Synchronization and coordination of art per- formances in highly competitive contexts: Battle scenes of expert break- dancers,’’Front. Psychol., vol. 12, Art. no. 635534, Apr. 2021, 10.3389/fp- syg.2021.635534

    work page doi:10.3389/fp- 2021

  10. [10]

    Shimizu and T

    D. Shimizu and T. Okada, ‘‘Coordination dynamics of back-and-forth movement among expert performers: Interaction in the battle scene of breaking,’’Front. Psychol., vol. 16, Art. no. 1441378, Jul. 2025, 10.3389/fpsyg.2025.1441378

    work page doi:10.3389/fpsyg.2025.1441378 2025

  11. [11]

    Buck and E

    J. Buck and E. Buck, ‘‘Biology of synchronous flashing of fireflies,’’ Nature, vol. 211, pp. 562–564, Aug. 1966, 10.1038/211562a0

    work page doi:10.1038/211562a0 1966

  12. [12]

    Y . Ma, E. W. M. Lee, M. Shi, and R. K. K. Y uen, ‘‘Spontaneous synchro- nization of motion in pedestrian crowds of different densities,’’Nat. Hum. Behav., vol. 5, pp. 447–457, Jan. 2021, 10.1038/s41562-020-00997-3. 6 VOLUME 11, 2023 Ichikawaet al.: Movement Synchronization in Complex and Dynamic Team Coordination TABLE 1.Estimations of Bayesian inference...

    work page doi:10.1038/s41562-020-00997-3 2021

  13. [13]

    Z. Néda, E. Ravasz, Y . Brechet, T. Vicsek, and A. L. Barabási, ‘‘The sound of many hands clapping,’’Nature, vol. 403, pp. 849–850, Feb. 2000, 10.1038/35002660

    work page doi:10.1038/35002660 2000

  14. [14]

    A. R. Willms, P . M. Kitanov, and W. F. Langford, ‘‘Huygens’ clocks revisited,’’R. Soc. Open Sci., vol. 4, Art. no. 170777, Sep. 2017, 10.1098/rsos.170777

    work page doi:10.1098/rsos.170777 2017

  15. [15]

    Kuramoto and I

    Y . Kuramoto and I. Nishikawa, ‘‘Statistical macrodynamics of large dy- namical systems: Case of a phase transition in oscillator communities,’’J. Stat. Phys., vol. 49, pp. 569–605, Nov. 1987, 10.1007/BF01009349

    work page doi:10.1007/bf01009349 1987

  16. [16]

    R. C. Schmidt, C. Carello, and M. T. Turvey, ‘‘Phase transitions and critical fluctuations in the visual coordination of rhythmic movements between people,’’J. Exp. Psychol. Hum. Percept. Perform., vol. 16, no. 2, pp. 227– 247, May 1990, 10.1037/0096-1523.16.2.227

    work page doi:10.1037/0096-1523.16.2.227 1990

  17. [17]

    Launay, B

    J. Launay, B. Tarr, and R. I. Dunbar, ‘‘Synchrony as an adaptive mechanism for large-scale human social bonding,’’Ethology, vol. 122, no. 10, pp. 779– 789, Aug. 2016, 10.1111/eth.12528

    work page doi:10.1111/eth.12528 2016

  18. [18]

    V aldesolo and D

    P . V aldesolo and D. DeSteno, ‘‘Synchrony and the social tuning of compas- sion,’’Emotion, vol. 11, no. 2, pp. 262–266, Apr. 2011, 10.1037/a0021302

    work page doi:10.1037/a0021302 2011

  19. [19]

    S. S. Wiltermuth and C. Heath, ‘‘Synchrony and cooperation,’’Psychol. Sci., vol. 20, no. 1, pp.1–5, Jan. 2009, 10.1111/j.1467-9280.2008.02253.x

    work page doi:10.1111/j.1467-9280.2008.02253.x 2009

  20. [20]

    L. Y an, C. Na, and J. Kang, ‘‘The impact of team synchrony on argument construction and science knowledge acquisition: Insights from a science learning game,’’J. Sci. Educ. Technol., vol. 33, pp. 633–646, May 2024, 10.1007/s10956-024-10110-2

    work page doi:10.1007/s10956-024-10110-2 2024

  21. [21]

    Y okoyama and Y

    K. Y okoyama and Y . Y amamoto, ‘‘Three people can synchronize as cou- pled oscillators during sports activities,’’PLoS Comput. Biol., vol. 7, no. 10, Art. no. e1002181, Oct. 2011, 10.1371/journal.pcbi.1002181

    work page doi:10.1371/journal.pcbi.1002181 2011

  22. [22]

    Kijima, K

    A. Kijima, K. Kadota, K. Y okoyama, M. Okumura, H. Suzuki, R. C. Schmidt, and Y . Y amamoto, ‘‘Switching dynamics in an interpersonal com- petition brings about ‘‘deadlock’’ synchronization of players,’’PLoS One, vol. 7, no. 11, Art. no. e47911, Nov. 2012, 10.1371/journal.pone.0047911

    work page doi:10.1371/journal.pone.0047911 2012

  23. [23]

    Okumura, A

    M. Okumura, A. Kijima, K. Kadota, K. Y okoyama, H. Suzuki, and Y . Y amamoto, ‘‘A critical interpersonal distance switches between two co- ordination modes in Kendo matches,’’PLoS One, vol. 7, no. 12, Art. no. e51877, Dec. 2012, 10.1371/journal.pone.0051877

    work page doi:10.1371/journal.pone.0051877 2012

  24. [24]

    Santos, J

    R. Santos, J. Garganta, R. J. Lopes, K. Davids, and J. Ribeiro, ‘‘Capturing homeostatic behaviour in elite football teams: Synchronisation tendencies of cooperative and oppositional dynamics,’’Int. J. Data Sci. Anal., vol. 21, Art. no. 10, Nov. 2025, 10.1007/s41060-025-00970-1

    work page doi:10.1007/s41060-025-00970-1 2025

  25. [25]

    Ichikawa, M

    J. Ichikawa, M. Y amada, and K. Fujii, ‘‘Analyzing coordinated group behavior through role-sharing: A pilot study in female 3-on-3 basketball with practical application,’’Front. Sports Act. Living, vol. 7, Art. no. 1513982, May 2025, 10.3389/fspor.2025.1513982

    work page doi:10.3389/fspor.2025.1513982 2025

  26. [26]

    Ichikawa, M

    J. Ichikawa, M. Y amada, Y . Iwaihara, G. Ichinose, and K. Fujii, ‘‘Applying team strategies for dynamic coordination: A comparative study of expertise using 3-on-3 basketball,’’PLoS One, vol. 21, no. 2, Art. no. e0343077, Feb. 2026, 10.1371/journal.pone.0343077

    work page doi:10.1371/journal.pone.0343077 2026

  27. [27]

    Ichikawa, M

    J. Ichikawa, M. Y amada, K. Fujii, and Y . Takeuchi, ‘‘Understanding role- sharing in coordinated group interaction: Proposal of an integrative group processing model based on top-down and bottom-up mechanisms,’’Cog- nitive Studies: Bulletin of the Japanese Cognitive Science Society, vol. 33, no.2, Jun. 2026, 10.11225/cs.2025.059

    work page doi:10.11225/cs.2025.059 2026

  28. [28]

    J. K. Kruschke, ‘‘Bayesian data analysis,’’Cogn. Sci., vol. 1, no. 5, pp. 658–676, Apr. 2010, 10.1002/wcs.72

    work page doi:10.1002/wcs.72 2010

  29. [29]

    J. K. Kruschke, ‘‘Bayesian estimation supersedes the t test,’’J. Exp. Psy- chol. Gen., vol. 142, no. 2, pp. 573–603, May 2013, 10.1037/a0029146

    work page doi:10.1037/a0029146 2013

  30. [30]

    M. F. da Silva, Á. Novillo, A. Aleta, R. López del Campo, R. Resta, Y . Moreno, and J. M. Buldú, ‘‘The interplay between pitch control and top speed in soccer considering the stamina factor,’’Sci. Rep., vol. 15, Art. no. 38799, Nov. 2025, 10.1038/s41598-025-22778-6

    work page doi:10.1038/s41598-025-22778-6 2025

  31. [31]

    Y . Wu, D. Deng, X. Xie, M. He, J. Xu, H. Zhang, H. Zhang, and Y . Wu, ‘‘OBTracker: Visual analytics of off-ball movements in basketball,’’ IEEE Trans. Vis. Comput. Graph., vol. 29, no. 1, pp. 929–939, Sep. 2022, 10.1109/TVCG.2022.3209373

    work page doi:10.1109/tvcg.2022.3209373 2022

  32. [32]

    Y agi, J

    S. Y agi, J. Ichikawa, and G. Ichinose, ‘‘Analysis of line break prediction models for detecting defensive breakthrough in football,’’arXiv, Oct. 2025, 10.48550/arXiv.2511.00121. VOLUME 11, 2023 7 Ichikawaet al.: Movement Synchronization in Complex and Dynamic Team Coordination FIGURE 5.Probabilities that the frequencies in the posttest condition exceeded...

    work page doi:10.48550/arxiv.2511.00121 2025