Confronta i metodi

	Leisure Time-Use Sequence Analysis	Sequential Behavior Analysis in Sport
Campo	Sport Leisure Studies	Sport Leisure Studies
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2000	1997
Ideatore	Andrew Abbott & Angela Tsay (optimal matching in sociology); applied to time-use leisure sequences	Roger Bakeman & John M. Gottman
Tipo	Order-aware pipeline for clustering daily leisure activity sequences	Sequential pipeline for transition probabilities of coded behavior streams
Fonte seminale	Abbott, A., & Tsay, A. (2000). Sequence Analysis and Optimal Matching Methods in Sociology: Review and Prospect. Sociological Methods & Research, 29(1), 3-33.	Bakeman, R., & Gottman, J. M. (1997). Observing Interaction: An Introduction to Sequential Analysis (2nd ed.). Cambridge: Cambridge University Press. ISBN: 9780521574273
Alias	Leisure Day Sequence Analysis, Optimal Matching of Leisure Episodes, Activity Sequence Analysis, Time-Use Optimal Matching	Lag Sequential Analysis, Sequential Pattern Analysis, Transition Probability Analysis, T-Pattern Analysis
Correlati	3	3
Sintesi	Leisure time-use sequence analysis treats a person's day not as a bundle of activity totals but as an ordered sequence of states, and asks which whole-day patterns of leisure recur across a population. It imports optimal matching -- the alignment technique Andrew Abbott and Angela Tsay reviewed for sociology -- into the study of time-use diaries: each day becomes a string of categorical states (sport, active leisure, passive leisure, work, sleep, and so on) sampled at regular intervals, and the dissimilarity between any two days is the minimum cost of editing one sequence into the other. Clustering the resulting dissimilarity matrix yields a typology of leisure days -- the active morning, the evening screen-leisure pattern, the fragmented weekend -- that preserves the timing and ordering of activity that simple duration tallies discard.	Sequential behavior analysis treats a sporting performance not as a bag of independent events but as an ordered stream in which what happens next depends on what just happened. Drawing on Roger Bakeman and John Gottman's authoritative 1997 text Observing Interaction: An Introduction to Sequential Analysis, the method codes play into a time-ordered sequence of mutually exclusive events, builds a transition matrix counting how often each event is followed by each other event at a given lag, and converts these counts into conditional transition probabilities. Crucially, it tests those probabilities against what would be expected by chance, so that genuinely recurrent patterns of play — the move that reliably leads to a shot, the defensive action that triggers a turnover — can be distinguished from coincidence. Hughes and Bartlett's performance-indicator framework supplies the bridge from these tested sequences to actionable tactical knowledge.