This study investigated a method for modeling a landscape of opportunities for penetrative passing completed on the ground by ball carriers in association football.Analysis of video footage of competitive, professional football performance was undertaken, identifying a sample (n=20) of attacking sub-phases of game play which ended in a penetrative pass being made between defenders to a receiver. Players' relative co-positioning during performance was modelled using bi-dimensional x and y coordinates of each player recorded at 25 fps. Data on player movements during competitive interactions were captured using an automatic video tracking system, recording player co-locations emerging over time, as well as current and estimated running velocities. Results revealed that the half-spaces between the midfield and both side lines were the key locations on field providing most affordances for penetrating passes in the competitive performance sample analysed. Due to the dynamics of players' co-adaptive performance behaviours, it was expected that opportunities for penetrative passing by ball carriers would not display a homogeneous space-time spread across the entire field. Results agreed with these expectations, showing how a landscape of opportunities for penetrative passing might be specified by information emerging from continuous player interactions in competitive performance.
This study aims to illustrate the landscape of passing opportunities of a football team across a set of competitive matches. To do so positional data of 5 competitive matches was used to create polygons of pass availability. Passes were divided into three types depending on the hypothetical threat they may pose to the opposing defense (penetrative, support, and backwards passes). These categories were used to create three heatmaps per match. Moreover, the mean time of passing opportunities was calculated and compared across matches and for the three categories of passes. Due to the specificity of player’s interactive behavior, results showed heatmaps with a variety of patterns. Specifically the fifth match was very dissimilar to the other four. However, characterizing a football match in terms of passing opportunities with a single heatmap dismisses the variety of dynamics that occur throughout a match. Therefore, three temporal heatmaps over windows of 10 min were presented highlighting on-going dynamical changes in pass availability. Results also display that penetrative passes were available over shorter periods of time than backward passes that were available shorter than support passes. The results highlight the sensibility of the model to different task constrains that emerge within football matches.
The recently approved regulation on Energy Communities in Europe is paving the way for new collective forms of energy consumption and production, mainly based on photovoltaics. However, energy modeling approaches that can adequately evaluate the impact of these new regulations on energy community configurations are still lacking, particularly with regards to the grid tariffs imposed on collective systems. Thus, the present work models three different energy community configurations sustained on collective photovoltaics self-consumption for a small city in southern Portugal. This energy community, which integrates the city consumers and a local winery, was modeled using the Python-based Calliope framework. Using real electricity demand data from power transformers and an actual winery, the techno-economic feasibility of each configuration was assessed. Results show that all collective arrangements can promote a higher penetration of photovoltaic capacity (up to 23%) and a modest reduction in the overall cost of electricity (up to 8%). However, there are clear trade-offs between the different pathways: more centralized configurations have 53% lower installation costs but are more sensitive to grid use costs (which can represent up to 74% of the total system costs). Moreover, key actor’s individual self-consumption rate may decrease by 10% in order to benefit the energy community as a whole.
Spatio-temporal solar forecasting based on statistical models seldom integrates wind information. An AutoRegressive with eXogenous input (ARX) model was tested using global horizontal irradiation records from a set of pyranometers deployed in Oahu, Hawaii, USA, where northeasterly winds are predominant. When irradiance is forecasted 10-s ahead, interesting forecast skills (up to 30.8%) can be achieved when a site has upwind neighbors available. However, when forecast skill is mapped as a function of wind direction at 850 hPa (from an ERA 5 reanalysis), negative skill values can be observed when nondominant winds occur. A wind regime-based approach is proposed, where different ARX models are built for different wind direction intervals, which substantially improves the forecasting accuracy for the underperforming wind directions. When the regime definition also takes into account wind speed, the ARX model detects spatial patterns for faster winds, with several nondominant directions achieving skill scores higher than 20%. Replacing the wind reanalysis by historical forecasts from ERA 5 reduced the overall skill by less than 0.1%.
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