Performance Streaks in Elite Beach Volleyball - Does Failure in One Sideout Affect Attacking in the Next?

Abstract

This study explores the influence of sideout failure on performance in the next sideout in beach volleyball. The sample comprises 965 elite matches in the FIVB World Series 2012–2016 and in the Olympic Games 2012/2016 including 28,974 sideout sequences (12,755 for men and 16,219 for women). A sideout sequence consists of two sideouts by the same player during the same set in a timeframe of four rallies. The first sideout in this sequence is referred to as the previous sideout and the second sideout as the next sideout. After misses, -tests indicate a significantly higher technique alternation rate (from spike to shot or vice versa) in the next sideouts for both men (+32.7%) and women (+40.4%) than the next sideouts after hits. After shot misses, the share of shots in the next sideouts was −12.9% lower for men and −8.3% lower for women than the next sideouts after shot hits. After spike misses, the share of shots in the next sideouts by female players was +5.5% significantly higher, and shot hit rate was −6.5% lower than the next sideouts after spike hits. These findings support the belief that tactical decisions and performance in top-level beach volleyball are influenced by failure in the previous sideouts. They might support coaches and players when analyzing matches and developing game strategies.

To err again is human: exploring a bidirectional relationship between pressure and performance failure feedback

“…Negative feedback (e.g., perceived errors) may provide a stronger input to subsequent performance expectancies than positive feedback (e.g., hot streaks). This interpretation is supported by recent data in volleyball decision making (Link & Wenninger, 2019) and the work of Baumeister and colleagues, who intimated that "bad is stronger than good", as a general principle across a broad range of psychological phenomena (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001).…”

“…Dependence reflects the belief that the probability of success on one play is influenced by previous plays and is most frequently associated with research examining performance streaks; colloquially referred to as the hot hand effect (e.g., Bar-Eli, Avugos, & Raab, 2006;Wetzels et al, 2016). However, interpreting negative performance feedback (e.g., an error) as evidence that more mistakes are likely, would also reflect dependence (e.g., Link & Wenninger, 2019). To summarize, ACTS predicts that when both the perceived cost of failure (influenced by fluctuations in the current level of pressure) and perceived probability of failure (influenced by previous unsuccessful performance feedback) are high, the interactive effect will lead to heightened anxiety, impaired attentional control and negative consequences for performance (as summarized in Figure 1).…”

Global Position Analysis during Official Elite Female Beach Volleyball Competition: A Pilot Study

“…The standard deviation observed in the distance covered in our study might reflect sets debated between more unbalanced teams [7]. Female BV is characterized by longer rallies than for male BV [22,23], which could highlight the total distance covered and Player Load. Addition-ally, variables that depend on total distance to express different levels of intensity (displacement walking, jogging and running, acceleration, deceleration, and jumps) should not be used to compare players, sets or studies unless they are expressed by the percentage of the total distance, acceleration, deceleration, and fly-time (jumps).…”

Experts use base rates in real-world sequential decisions

“…A miss does not imply that the sideout team did not win the rally, since there is still the possibility of defending the counterattack and scoring. The performance variables selected are standard in scouting reports (Link & Wenninger, 2019).…”

“…All data were annotated by professional beach volleyball analysts using custom-made observation software for use with video recordings (Link, 2014). The data are part of a more detailed data set that was used to prepare Germany's national teams for their competitions and has already been used in other publications (Link & Wenninger, 2019;Wenninger et al, 2020). Cohen's kappa statistics show substantial to perfect agreement between two observers for the variables selected player and outcome based on a subset of 130 sideouts (κ = .94 to 1.0).…”