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BackgroundInjuries in association football (soccer) are debilitating for players and can also be detrimental to the success of a team or club. The type or condition of a playing surface has been empirically linked to injuries, yet results are inconclusive. The overall purpose of this study was to analyse elite football players’ perceived links between playing surfaces and injury from a worldwide cohort of players. The results of this study can help to inform areas for future playing surface research aimed at trying to alleviate user concerns and meet user (i.e. the player) needs.MethodsQuantitative data were collected from 1129 players across the globe to address the aim of this study.ResultsNinety-one percent of players believed the type or condition of a surface could increase injury risk. Abrasive injuries, along with soreness and pain, were perceived to be greater on artificial turf. Surface type, surface properties and age were all potential risk factors identified by the players and linked to the playing surfaces.ConclusionsThe results identified three areas where future research should be focussed to help develop surfaces that alleviate user concerns and meet user (i.e. player) needs: (i) current reporting of soreness, pain or fatigue as injuries, (ii) contribution of surface properties to injury; and (iii) surface experience of players from different countries differentiates their views of injury risk.

Wheelchair basketball coaches and researchers have typically relied on box score data and the Comprehensive Basketball Grading System to inform practice, however, these data do not acknowledge how the dynamic perspectives of teams change, vary and adapt during possessions in relation to the outcome of a game. Therefore, this study aimed to identify the key dynamic variables associated with team success in elite men’s wheelchair basketball and explore the impact of each key dynamic variable upon the outcome of performance through the use of binary logistic regression modeling. The valid and reliable template developed Francis et al. (2019) was used to analyze video footage in SportsCode from 31 games at the men’s 2015 European Wheelchair Basketball Championships. The 31 games resulted in 6,126 rows of data which were exported and converted into a CSV file, analyzed using R ( R Core Team, 2015 ) and subjected to a data modeling process. Chi-square analyses identified significant ( p < 0.05) relationships between Game Outcome and 19 Categorical Predictor Variables. Automated stepwise binary regression model building was completed using 70% of the data (4,282 possessions) and produced a model that included 12 Categorical Predictor Variables. The accuracy of the developed model was deemed to be acceptable at accurately predicting the remaining 30% of the data (1,844 possessions) and produced an area under the receiver operating characteristic curve value of 0.759. The model identified the odds of winning are more than double when the team in possession are in a state of winning at the start of the possession are increased five-fold when the offensive team do not use a 1.0 or 1.5 classified player, but are increased six-fold when the offensive team use three or more 3.0 or 3.5 players. The final model can be used by coaches, players and support staff to devise training and game strategies that involve selecting the most appropriate offensive and defensive approaches when performing ball possessions to enhance the likelihood of winning in elite men’s wheelchair basketball.

This study aimed to develop a valid and reliable performance analysis template for quantifying team action variables in elite men’s wheelchair basketball. First action variables and operational definitions were identified by the authors and verified by an expert panel of wheelchair basketball coaching staff in order to establish expert validity. A total of 109 action variable were then placed into 17 agreed Categorical Predictor Variable categories. The action variables were then used to develop a computerized performance analysis template for post-event analysis. Each possession (n = 200) from an international men’s wheelchair basketball game was analyzed by the first author on two occasions for assessment of intra-observer reliability and by a coach and a performance analyst for inter-observer reliability. Percentage error and Weighted Kappa coefficients were calculated to compare the levels of error and agreement for each action variable. Intra-observer reliability demonstrated perfect or almost perfect agreement (

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