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Dan Slott takes on half of the Fantastic Four - and, naturally, the ever-amazing writer brings Spider-Man along for the ride! First, the Human Torch and Spidey's unique friendship is explored across five adventures in different eras. From high school to adulthood, from the Coffee Bean to the Negative Zone, from Dorrie Evans to Mary Jane Watson, it's the ultimate team-up of Flamebrain and Webhead! Then, the ever-lovin' blue-eyed Thing takes the spotlight! The idol of millions is now worth billions, and he's enjoying his newfound riches - not to mention the odd game of poker - with pals including the FF, Goliath, Hercules, Lockjaw and, yes, Spider-Man! But fear not - bashful Benjy has reserved a clobberin' time or two for foes like Arcade, Shockjock and classic sparring partners Trapster and Sandman!

Knowledge of optimal technical performance is used to determine match strategy and the design of training programs. Previous studies in men's soccer have identified certain technical characteristics that are related to success. These studies however, have relative limited sample sizes or limited ranges of performance indicators, which may have limited the analytical approaches that were used. Research in women's soccer and our understanding of optimal technical performance, is even more limited (n = 3). Therefore, the aim of this study was to identify technical determinants of match outcome in the women's game and to compare analytical approaches using a large sample size (n = 1390 team performances) and range of variables (n = 450). Three different analytical approaches (i.e. combinations of technical performance variables) were used, a data-driven approach, a rational approach and an approach based on the literature in men's soccer. Match outcome was modelled using variables from each analytical approach, using generalised linear modelling and decision trees. It was found that the rational and data-driven approaches outperformed the literature-driven approach in predicting match outcome. The strongest determinants of match outcome were; scoring first, intentional assists relative to the opponent, the percentage of shots on goal saved by the goalkeeper relative to the opponent, shots on goal relative to the opponent and the percentage of duels that are successful. Moreover the rational and data-driven approach achieved higher prediction accuracies than comparable studies about men's soccer.

To understand the historical context of and relationships between, the characteristics of shots at goal and match outcome in the Australian Football League. Observational. Descriptive statistics of shots at goal were calculated and compared across 20 seasons. The location, type, and outcome of all shots at goal (n = 43,254), by all teams (2017–19 & 21), were compared with match outcome. The total number of shots per match and the accuracy of shots haven't changed in two decades. Most teams win by having more shots at goal (Wilcoxon-r effect size 0.63) than their opponent (i.e. “majority strategy”) and of these, the number of open shots (0.48) is slightly more important than set shots (0.43), followed by shot accuracy (0.29). However, some teams (14 %) win by taking fewer shots at goal from field locations with a higher likelihood of scoring a goal (i.e. “minority strategy”). Arc angle and shot type can be used to predict the outcome of a shot at goal with 60.3 % classification accuracy. The novel shot-outcome prediction model reported here provides a better opportunity to evaluate goal kicking performance of teams and players, because it accounts for the type and difficulty of the shot. Teams can evaluate the shot accuracy of their players more fairly, by accounting for shot location using the method reported here. Coaches can compare the two shot strategies identified and implement the one that suits the skill profile of their players and increase their likelihood of winning.

Understanding reliability of performance analysis tools is important to ensure match to match comparisons can be undertaken with the knowledge of consistency between coding situations. There are few published studies examining the reliability of commonly used performance analysis tools. The aim of this project was to assess the inter- and intra-rater reliability of the NetballStats application and to make comparisons between live and video-based coding situations. Two ‘coders’ coded eight netball matches using the NetballStats application, coding each match live, then twice from video. Level of agreement was assessed for frequency counts across the variables coded. Results showed that intra-rater agreement was higher than inter-rater agreement and that reliability from video coding is better than from live coding. High frequency events automatically coded by the application and events that are well defined had greater levels of agreement than lower frequency events and subjectively judged events. Live coding situations underrepresent occurrence of events, particularly for high frequency events such as ‘possession’. To ensure reliability between coders, clubs should provide an extensive training program to coders with clear instructions on coding subjective events. Coaches should be aware that live coding underestimates some event types and factor this into their decision making processes.

Studies of training and competition load in sport are usually based on data that represents a sample of a league and or annual training program. These studies sometimes explore important factors that are affected by load, such as training adaptations and injury risk. The generalisability of the conclusions of these studies, can depend on how much load varies between seasons, training phases and teams. The interpretation of previous load studies and the design of future load studies should be influenced by an understanding of how load can vary across seasons, training phases and between teams. The current study compared training loads (session rating of perceived exertion x session duration) between all (8) teams in an elite Netball competition for multiple (2) season phases and (2) seasons. A total of 29,545 records of athlete session training loads were included in the analysis. Linear mixed models identified differences between seasons and training phases (p < .05). There were also differences between teams and a complex set of interactions between these three factors (season, phase, and team) (p < .05). While the absolute value of the training loads reported here are only relevant to elite netball, these results illustrate that when data is sampled from a broader context, the range and variation in load may increase. This highlights the importance of cautiously interpreting and generalisation of findings from load studies that use limited data sets.

Modern football prioritizes team play and tactical strategies over individual brilliance. However, its low-scoring nature makes evaluating team performance challenging. Unpredictable ball movement enhances offensive play while complicating defensive setups. To better capture this dynamic nature, authors’ prior work has proposed entropy-based time-series metric to assess unpredictable ball movement by quantifying Spatial Event Distribution Randomness (EDRan). However, some teams may prefer to dominate specific areas with unpredictability, while others utilize the entire field. Existing literature has not examined whether emphasizing dominant (frequently used field regions for ball movement) or considering all regions equally, including rarely used areas, is a more effective approach for computing randomness in event distribution. Moreover, existing research has not investigated the underlying patterns of event distribution randomness, particularly how these variations differ between winning and losing teams, both in terms of overall field coverage and concentration within dominant regions. This study addresses these gaps by analyzing event distribution randomness using Rényi entropy with varying alpha values ( 0 ≤ α ≤ 2 0).Correlation analysis indicated that assigning equal weight to all field regions, including rarely used areas, with Max entropy ( α = 0 alpha) was most strongly associated with match-winning performance. In men’s data, machine learning models trained with α = 0 , 0.1 , alpha and 0.5 achieved statistically significant improvements over models trained with the traditionally used Shannon entropy ( α → 1 alpha). These results suggest that unpredictability distributed across the entire field, maximizing the use of diverse regions, is more strongly associated with success than randomness restricted to dominant areas. The best-performing model, obtained with α = 0 alpha, significantly outperformed both the baseline and existing models in the literature, achieving an accuracy of 80.61% in predicting match winners.

To identify novel insights about performance in Australian Football (AF), by modelling the relationships between player actions and match outcomes. This study extends and improves on previous studies by utilising a wider range of performance indicators (PIs) and a longer time frame for the development of predictive models. Observational. Ninety-one team PIs from the 2001 to 2016 Australian Football League seasons were used as independent variables. The categorical Win–Loss and continuous Score Margin match outcome measures were used as dependent variables. Decision tree and Generalised Linear Models were created to describe the relationships between the values of the PIs and match outcome. Decision tree models predicted Win–Loss and Score Margin with up to 88.9% and 70.3% accuracy, respectively. The Generalised Linear Models predicted Score Margin to within 6.8 points (RMSE) and Win–Loss with up to 95.1% accuracy. The PIs that are most predictive of match outcome include; Turnovers Forced score, Inside 50s per shot, Metres Gained and Time in Possession, all in their relative (to opposition) form. The decision trees illustrate how combinations of the values of these PIs are associated with match outcome, and they indicate target values for these PIs. This work used a wider range of PIs and more historical data than previous reports and consequently demonstrated higher prediction accuracies and additional insights about important indicators of performance. The methods used in this work can be implemented by other sport analysts to generate further insights that support the strategic decision-making processes of coaches.

The purpose of this study was to provide a profile of the demographics and employment characteristics of the Australian high performance and sport science workforce. This study used a cross-sectional, quantitative survey methodology to collect data about the Australian high performance and sport science workforce. 175 Australian high performance and sport science employees completed an online survey which captured demographic information and work-related characteristics such as role, industry sector, income, permanence of employment and hours worked. Descriptive statistics were used to summarise information and some comparisons were made between position titles, industry sectors and sexes. The Australian high performance and sport science workforce is predominantly male (76.0%), ≤35 years of age (50.3%), located on the eastern seaboard of Australia (69%) and have been in their current position for 2–5 years (37.4%). They are mostly employed on a fixed term contract of 2.4 years, by an institute of sport. Income varied, with those working in professional sporting clubs and/or employed as high performance managers earning the highest wage. On average, participants worked well over their contracted hours, with a considerable proportion of these hours outside the standard 9–5 working week. Employees in the high performance and sport science workforce in Australia face significant professional issues that relate to long and unusual work hours, job insecurity and income disparity. Policy makers and the managers of this workforce should consider the impact of these issues on work-life balance, staff retention rates and the risk of burnout.

No study has investigated the influence of field position and phase of play on the physical demands of match-play in professional rugby league forwards. We investigated the physical demands placed on forwards in elite rugby league matches, with special reference to how these demands differed between attack and defence, and in different field positions. Cohort study. Twenty-two rugby league players (26±3 years) from the same professional club participated in this study. Global positioning system (GPS) analysis was completed during 23 matches. Video footage was synchronised with the GPS files and coded for the time spent in attack and defence, and in one of three different field positions (0–30m, 31–70m, 71–100m). The physical demands of defence were consistently greater than attack. Moderate to large differences (ES=0.62–1.41) were found between defence and attack for distance covered (109±16m/min vs. 82±12m/min), low speed distance (104±15m/min vs. 78±11m/min), frequency of collisions (1.9±0.7/min vs. 0.8±0.3/min), and repeated high-intensity effort bouts (1 every 4.9±5.1min vs. 1 every 9.4±6.1min). The running demands and frequency of repeated high-intensity effort bouts were greater when defending in the opposition's 30m zone (i.e. 71–100m), with repeated high-intensity effort bouts also occurring more frequently when defending the team's own try-line and when attacking the opposition's try-line. Specific training drills designed to replicate the attacking and defensive demands of different field positional zones are likely to be effective in preparing players for the most demanding activities that occur in professional rugby league match-play.

Understanding the successful characteristics of team formation during different scenarios in Australian Football matches can assist coaches in making important tactical match-day and training decisions. The aims of this study were to explore the outcomes of entries inside 50 m of the goal, in Australian Football and to determine whether there was an association between team formation and team defensive performance after a turnover. Observational. Global Positioning System (GPS) data, technical event data and video files from 22 matches in one season were obtained from an elite Australian Football club. Of 1092 forward 50 entries, 392 possession chains that resulted in a turnover were analysed. Variables representing team formation of players at the occurrence of turnover were compared between positive and negative outcomes of the subsequent possession chain. Logistic regression and decision tree modelling were also used to explore associations and variable importance. None of 18 team formation characteristics differed between positive and negative outcomes of turnovers. Multivariate modelling identified that having a team formation with greater width than length made it more likely to result in a positive outcome (Decision tree classification accuracy = 69.5%, AUROC = 0.72). No single characteristic of team formation affects the outcome of a turnover possession chain, however team formation that was wider than it was long may be associated with a more desirable outcome. The lack of association between most team formation characteristics and defensive outcomes, highlight the risk of over emphasising team formation in tactical planning for some phases of play.