Educational serious games are primarily intended to teach about or train on a subject. However, a serious game must also be catchy for the player to want to play it multiple times and thus learn while playing. The design of educational serious games includes game experts and pedagogical experts that must be able to efficiently communicate to produce a product that is both educationally efficient and fun to play. Although there are some design frameworks to help with this communication, they are usually more conceptual and do not distinguish the fun factor from the learning contents well, making communication difficult. In this paper, a new practical methodology is presented to support the design process of this kind of digital games. This methodology is more all-encompassing because it identifies all the main steps that are needed to define the learning mechanisms in an educational serious game, from topic choice to user experience. It also separates the game’s learning contents from other mechanics used to keep the game fun to play. Finally, some practical examples are shown, illustrating the use of this methodology.

In computer science, an ontology is a way of showing the properties of a subject area and how they are related by defining a set of concepts and categories that represent the subject. There have been many attempts to create a widely accepted ontology for the universe of games. Most of these attempts are defined based on an analytical perspective: few have found frequent use outside universities, as they are not easily translated to the development of games, which is a design perspective. There are some core aspects of the domain that turn this task into a difficult goal to achieve. In addition, game designers tend to refuse a methodology or a structured way of developing a game; the main concern is that it can impair creativity in a field that could not survive without it. A defined ontology would improve and mature the growing industry of digital games, both by enhancing the understanding of the domain and by supporting a structured methodology for designing games. This paper describes the properties of digital games and shows how they make it difficult to create an ontology for that field of study, especially when it comes to a design perspective. It clarifies the closest approach to a unified ontology that there is for the game domain: the mechanics, dynamics and aesthetics framework (MDA). We propose the redefinition of MDA’s taxonomy, calling it Redefining the MDA (RMDA), providing better use for the approach from a designer’s perspective, embracing the design properties of the domain, and overcoming issues found in the literature of the game domain. The main purpose of this paper is to clarify the MDA framework by redefining its main components, mechanics, dynamics and aesthetics, as a way to make the tool more understandable and useful for game designers. Understanding aesthetics and how developers can invoke them by correctly defining mechanics and creating dynamics is the main focus of the paper. Thus, some examples are provided in order to explain the applicability of the RMDA as a methodology to produce games.

The development of a serious game requires perfect knowledge of the learning domain to obtain the desired results. But it is also true that this may not be enough to develop a successful serious game. First of all, the player has to feel that he is playing a game where the learning is only a consequence of the playing actions. Otherwise, the game is viewed as boring and not as a fun activity and engaging. For example, the player can catch some items in the scenario and then separate them according to its type (i.e., recycle them). Thus, the main action for player is catching the items in the scenario where the recycle action is a second action, which is viewed as a consequence of the first action. Sometimes, the game design relies on a detailed approach based on the ideas of the developers because some educational content are difficult to integrate in the games, while maintaining the fun factor in the first place. In this paper we propose a new methodology of design and development of serious games that facilitates the integration of educational contents in the games. Furthermore, we present a serious game, called “Clean World”, created using this new methodology.

Pattern analysis is a well-established topic in team sports performance analysis, and is usually centered on the analysis of passing sequences. Taking a Bayesian approach to the study of these interactions, this work presents novel entropy mathematical models for Markov chain-based pattern analysis in team sports networks, with Relative Transition Entropy and Network Transition Entropy applied to both passing and reception patterns. To demonstrate their applicability, these mathematical models were used in a case study in football—the 2016/2017 Champions League Final, where both teams were analyzed. The results show that the winning team, Real Madrid, presented greater values for both individual and team transition entropies, which indicate that greater levels of unpredictability may bring teams closer to victory. In conclusion, these metrics may provide information to game analysts, allowing them to provide coaches with accurate and timely information about the key players of the game.

Analysis of the physical, technical and physiological variations induced through the use of different soccer game formats have been widely discussed. However, the coaching justification for the specific use of certain game formats based on individual and collective spatial awareness is unclear. As a result, the purpose of this study was to analyze 11 versus 11 game formats conducted across two pitch sizes (half-size: 54 m × 68 m vs full-size: 108 m × 68 m) to identify effects of time–motion profiles, individual exploration behavior and collective organization. A total of 10 amateur soccer players from the same team (23.39 ± 3.91 years old) participated in this study. Data position of the players was used to calculate the spatial exploration index and the surface area. Distances covered in different speeds were used to observe the time–motion profile. The full-size pitch dimensions significantly contributed to greater distances covered via running (3.86–5.52 m s⁻¹) and sprinting (>5.52 m s⁻¹). Total distance and number of sprints were also significantly greater in the full-size pitch as compared to the half-size pitch. The surface area covered by the team (half-size pitch: 431.83 m² vs full-size pitch: 589.14 m²) was significantly larger in the full-size pitch condition. However, the reduced half-size pitch significantly contributed to a greater individual spatial exploration. Results of this study suggest that running and sprinting activities increase when large, full-size pitch dimensions are utilized. Smaller surface area half-size pitch contributes to a better exploration of the pitch measured by spatial exploration index while maintaining adequate surface area coverage by the team. In conclusion, the authors suggest that the small half-size pitch is more appropriate for low-intensity training sessions and field exploration for players in different positions. Alternatively, the large full-size pitch is more appropriate for greater physically demanding training sessions with players focused on positional tactical behavior.