The penalty kick might be a crucial moment during a soccer game because of its relevance on the scoreboard, mainly during shootouts. For this purpose, the present study established a model to predict the ball direction taking into consideration the leg dominance of the penalty taker and other posture variables just before the ball contact. A total of 412 penalties from 11 international high-standard tournaments (2010–2019) were analysed. The better predictive model for the shot direction was selected through all possible subsets regression method. Thus, the final model included three predictors and two multiplicative terms, with an acceptable classifying power with a specificity and sensitivity greater than 85%. This model was applied for predicting the probability to shot to the left and the right side of the goal in 12 different kicking patterns. The most frequent pattern was a right-footed taker with the left arm in a >90° abduction with a left orientation of the support foot (n = 122; 30%). Following this pattern, probability of shooting to the left or to the right was 0.970 (95% CI: 0.927–0.988) and 0.030 (95% CI: 0.012–0.073), respectively. The present study conceived a model as parsimonious as possible with three predictors easily available to the goalkeeper.

In volleyball, the effect of different factors on serve performance has usually been analyzed with traditional statistical techniques such as logistic regression or discriminant analysis. Purpose: In this study, two of the main models used in unsupervised machine learning (cluster and principal component analysis) were applied to achieve these objectives: (a) to create groups of players considering their serve coefficient, age, height, and team ranking, and (b) to identify which variables related to the serve (type and performance), the players (role, age, and height), and the teams (ranking, match location, and quality of opposition) most explained the total variance of the data during an entire women’s volleyball season. Method: A total of 20,936 serves were analyzed during the 132 matches played in the 2017–2018 season in the Liga Iberdrola (women Spanish first division). The variables were related to the serving action (type of serve and performance), the players’ traits (player role, age, and height), and the teams’ characteristics (final ranking, match location, quality of opposition, and tournament). Results: Cluster analysis showed five groups of players differing in age, serve coefficient, team ranking, and height. Principal component analysis showed how the first five components explained 72.12% of the total variance. From these components, serve coefficient, team ranking, match location, quality of opposition, and player role each contributed more than 10%. Conclusions: These findings can help coaches to improve talent selection and players’ development according to competition demands.

The primary purpose of the present study was to compare the home advantage (HA) and the home team performance in the most relevant European rink hockey leagues (Spanish, Portuguese and Italian), considering the presence or absence of spectators in the competition venues due to the effect of COVID-19 restrictions. The sample was composed of 1665 rink hockey matches (654 from the Spanish league, 497 from the Portuguese league, and 514 from the Italian league) played between the 2018–2019 and 2020–2021 seasons. The HA and match variables comparisons were established using several negative binomial regression models. Results showed that the effect of HA did not disappear despite playing without spectators but decreased from 63.99% to 57.41% (p = 0.002). Moreover, the comparison of the match variables showed that playing with spectators benefited local teams’ performance, especially in the Portuguese and Italian leagues. Playing with spectators favoured local team performance in rink hockey matches, which is more evident in some analysed leagues. However, as HA does not disappear entirely without spectators, it is necessary to study other relevant performance factors that are not directly or indirectly attributable to crowd behaviour in rink hockey performance analyses.

The aim of this study was to check if the rally length in high-level Spanish volleyball was longer in women than in men. A total of 1,786 rallies were observed: 792 for women and 994 for men. The recorded variables were match (quarter-final 1, quarter-final 2, semi-final 1, semi-final 2, final), gender (men, women), rally length (seconds), pseudo-rally (no, yes), and terminal event (ball out of sight, ball in/out, fault). Different non-parametric statistical techniques were used to compare the rally length between groups or subsets of data: Kruskal-Wallis H test, Mann-Whitney U test, quantile regression, and survival analysis. The mean and median rally length was significantly and slightly longer in women than in men. The rally length difference between genders was barely 1 s in quantile .5 or median, while in quantile .95 it was just over 4 s. In women, the probability of ending the rally at 3.9, 5.1, 10.2, and 43.9 s (at 4.4, 6.3, 11.6, and 43.9 s without pseudo-rallies) was 25%, 50%, 75%, and 100%, respectively. In men, the probability of ending the rally at 3.2, 4.3, 7.9, and 29.1 s (at 3.9, 4.8, 8.8, and 29.1 s without pseudo-rallies) was 25%, 50%, 75%, and 100%, respectively. These temporal thresholds can help volleyball coaches to train their players in a coherent manner.

The main objective of the present study was to develop a concise predictive model to determine the likelihood of winning in female rink hockey based on various situational variables. Additionally, the study aimed to assess the individual impact of each predictor on match outcomes. The analysis encompassed a dataset of 840 matches during five consecutive seasons (from 2018–2019 to 2022–2023) in the Spanish first division (OkLiga). Employing the comprehensive method of all possible regressions, the most effective predictive logistic model for match outcomes was identified. This entire model featured five categorical predictor variables (match location, team level, opponent level, scoring first, and match status at halftime) and one binary outcome variable (match outcome). Subsequently, the final model, which exhibited a sensitivity and specificity surpassing 80% for a cut-off point of 0.439, emerged. This model was applied to predict winning a match in 18 frequent situations determined from a two-step cluster analysis. Within this predictive framework, match status at halftime emerged as the most influential predictor impacting the match outcome, followed by opponent level, team level, and match location. The implications of our findings extend to rink hockey coaches and practitioners. Recognizing the significant impact of situational variables on match outcomes empowers them to customize game plans and design more specific strategies, thereby enhancing game understanding and elevating the overall performance.