For those researchers and practitioners interested in optimal performance in sport, feeling states are one of the most important predictors of performance. Feeling states are a broad category that encompasses affective (e.g., emotions, moods), somatic (e.g., physical sensations), and other forms of feeling. All of these terms are difficult to define, and there is no consensus for any of them. Among these feeling states, emotions play the most prominent role in predicting performance; and are also the most researched, best-defined, and most theoretically mature construct in the feeling/affect literature.
Feeling states are, however, only half of the feeling-performance relationship. The most prolific theory in the field is the idiosyncratic Individual Zones of Optimal Functioning theory (IZOF: Hanin, 1978, 1997, 2000a). More recently, Tenenbaum and colleagues have developed the Individual Affect Performance Zone (IAPZ; Kamata, Tenenbaum, & Hanin, 2002; Tenenbaum, Edmonds, & Eccles, 2008) In this study, both idiographic models were combined in the following ways. (1) Rather than describing feelings as continuous two-dimensional constructs (wherein players rate the pleasantness and arousal-level of their feeling state), discrete items (e.g., confidence, worry, calmness) were selected to create each profile. (2) Ordinal Logistic Regressions were conducted to develop probabilistic estimates for four feeling categories (based on positive-/negative-valence and functional/dysfunctional groupings).
Ten male college tennis players volunteered to participate in this study. Participants developed individualized profiles, and then monitored the intensity of each item in the profile during competitive intra-squad matches. In total, 918 observations were recorded. Ordinal logistic regressions were conducted to identify probabilistic performance curves.
Visual comparisons of individual zone profiles, qualitative comparison of the discrete items selected by each participant, and statistical analyses provided support for the validity of the combined IZOF-IAPZ method used in this dissertation as well as for the idiosyncratic nature of feeling-performance relationships. Statistical analysis identified 76.3% interindividual differences based on comparisons of the location and width of each feeling-performance zone. Intraindividual differences did not emerge across context: only 12.5% of the zone comparisons for serving and returning serve displayed significant differences. Ordinal Logistic Regression (OLR)-based models were generally accurate. The complete four-category (function-valence) model predicted performance correctly 63.5% of the time (more than twice that of chance); and the four "trigger" items chosen by each player as the most important for his performance were 66.4% accurate—the top three trigger items were accurate 70.0% of the time.
The dissertation offers important data in the field of IZOF research because a large sample was collected during performances, and it was shown that OLR may be used to develop probabilistic feeling-performance profiles using discrete feeling items. As such, the interaction of valence and function can be studied along with simple profiles with only three self-selected performance predictors.