The idea that the success rate of a team increases when playing home is broadly accepted and documented for a wide variety of sports. Investigations on the so-called “home advantage phenomenon” date back to the 70’s and ever since has attracted the attention of scholars and sport enthusiasts. These studies have been mainly focused on identifying the phenomenon and trying to correlate it with external factors such as crowd noise and referee bias. Much less is known about the effects of home advantage in the “microscopic” dynamics of the game (within the game) or possible team-specific and evolving features of this phenomenon. Here we present a detailed study of these previous features in the National Basketball Association (NBA). By analyzing play-by-play events of more than sixteen thousand games that span thirteen NBA seasons, we have found that home advantage affects the microscopic dynamics of the game by increasing the scoring rates and decreasing the time intervals between scores of teams playing home. We verified that these two features are different among the NBA teams, for instance, the scoring rate of the Cleveland Cavaliers team is increased ≈0.16 points per minute (on average the seasons 2004–05 to 2013–14) when playing home, whereas for the New Jersey Nets (now the Brooklyn Nets) this rate increases in only ≈0.04 points per minute. We further observed that these microscopic features have evolved over time in a non-trivial manner when analyzing the results team-by-team. However, after averaging over all teams some regularities emerge; in particular, we noticed that the average differences in the scoring rates and in the characteristic times (related to the time intervals between scores) have slightly decreased over time, suggesting a weakening of the phenomenon. This study thus adds evidence of the home advantage phenomenon and contributes to a deeper understanding of this effect over the course of games.

/pdf/the-advantage-of-playing-home-in-nba-microscopic-team-qblcu9hd99.pdf

The Advantage of Playing Home in NBA: Microscopic, Team-Specific and Evolving Features.

Despite growing interest in quantifying and modeling the scoring dynamics within professional sports games, relative little is known about what patterns or principles, if any, cut across different sports. Using a comprehensive data set of scoring events in nearly a dozen consecutive seasons of college and professional (American) football, professional hockey, and professional basketball, we identify several common patterns in scoring dynamics. Across these sports, scoring tempo---when scoring events occur---closely follows a common Poisson process, with a sport-specific rate. Similarly, scoring balance---how often a team wins an event---follows a common Bernoulli process, with a parameter that effectively varies with the size of the lead. Combining these processes within a generative model of gameplay, we find they both reproduce the observed dynamics in all four sports and accurately predict game outcomes. These results demonstrate common dynamical patterns underlying within-game scoring dynamics across professional team sports, and suggest specific mechanisms for driving them. We close with a brief discussion of the implications of our results for several popular hypotheses about sports dynamics.

/pdf/scoring-dynamics-across-professional-team-sports-tempo-c2iqn74ohy.pdf

Scoring dynamics across professional team sports: tempo, balance and predictability

Sports leagues depend on competitive equilibrium between teams to make it attractive for the fans; from that comes competitive balance. Thus, sport leagues have acquired different structures that determine their competitiveness. The aim of this paper is to compare the competitive balance of the NBA and the primary leagues in Europe, given the NBA as a whole as well as each of its conferences individually. The measurements utilised were the Hirschman-Herfindahl index and the C5 and C8 ratios. The results demonstrate a better competitive balance in the NBA as a whole than in the European leagues; however, the differences disappear when each conference is analysed as an individual league. We conclude, then, that given each conference individually, with structures that are similar to the European leagues, there is a competitive balance, taking into consideration the indicators we have used. However, the NBA includes limitations in the competition that favour the possibility of being more attractive for the fans.

/pdf/balance-competitivo-en-las-ligas-europeas-de-baloncesto-y-la-1za4rm7sjh.pdf

Balance Competitivo en las Ligas Europeas de Baloncesto y la NBA

Sport is one of the most important activities of humanity. Its relevance goes beyond the physical/psychological benefits that their practicing provides (an aspect that justifies in itself its importance in our current society). In fact, be it as actually playing some sport or just watching some games, it provides a huge means for socialization and education. Needless to say, the economical viewpoint is also enormously relevant: events such as the Olympic Games, the FIFA World Cup, as well as continental and national football or basketball leagues attract the interest of millions of people and the intervening parties/clubs handle budgets in the severalhundred million euro range. For these reasons, sport is an activity deservedly studied from multiple point of views. In this sense, complex systems provide a perspective of the foremost interest when it comes to analyze sports. Consider for example that team sports have a strong network component, since they are essentially a network developed along time and space, with nodes being players and links being passes or other interactions. The network paradigm allows for a strong quantitative description of a whole match, while at the same time offers some insight on how performance is achieved. This could result, eventually, in a prediction of game outcome according to network characteristics. Complex networks, and thus emergent behavior, appear also in other aspects. Indeed, there are many aspects in the dynamics of sports (related to scoring, performance, etc.) that can be analyzed from this general perspective in order to model the game, find hidden patterns, and phase transitions, etc. From an even more general viewpoint, the analysis can be extended beyond the actual game dynamics and tackle other sport-related issues: transfer networks, sport fans networks, and their dynamics, etc. The importance of the topic has justified the existence of several events devoted to it, e.g., [1]. This special issue is an extension of the workshop on complex systems in sports which was chaired by the editors, and held in Paris in August 2011 along within the European Conference on Artificial Life. There were five papers accepted in that workshop, and they were

Complex systems in sports: Introduction to the special issue

Aim. Describing the theoretical framework in which the study of complex systems
is based, and its application to team sport through basketball. Method. We studied
basketball from three different levels: the competition, the game and the team. In the fi rst
case we evaluated the uncertainty degree using the Shannon entropy, in order to fi gure out
the uncertainty present in basketball. In the second cased we studied 6150 games of the 
Pagina 46
De Saa Guerra, Y., Martin-Gonzalez, J.M. y Garcia Manso, J.M. (2014).
 El baloncesto desde la perspectiva de los sistemas complejos no-lineales.
Revista Espanola de Educacion Fisica y Deportes, 406, 45-55
NBA regular season, analyzing scoring and time between points. Finally we carried out
an approach to the team performance using network theory and their interactions: passes,
screens and space creations. Results. NBA and ACB show high levels of uncertainty. The
point difference acts as an order parameter. The high randomness degree that exists in
the most part of games, score difference lower than 11 points, points outs a high level of
uncertainty to the fi nal result. Some parameters such as passes, screens and space creations
can be interpreted as interactions among players and enables to analyze using network
theory. Conclusions. Basketball leagues can be understood as systems organized critically
because they remain between two situations, one of maximum randomness and other
completed ordered. In turn, teams (players network) try to overcome the game through selforganization,
based in different patterns of complex networks.

https://reefd.es/index.php/reefd/article/view/25/27

El baloncesto desde la perspectiva de los sistemas complejos no-lineales

在一场篮球比赛的抽象得分是一个高度有活力的、非线性的过程。NBA 队试着每个季节越来越有竞争力。例如，他们在世界上把最好的运动员合并到他们的花名同。这和另外的机制同时发生在激动的 NBA 游戏并且很少做得分过程可预言。这份报纸是学习在篮球比赛预定并且得分的行为。作者在五个 NBA 常规季节分析所有游戏(200506， 200607， 200708， 200809， 200910 ) ，为 6150 个游戏的一个总数。得分不一致地表现；因此，作者也在篮球游戏在点分析差别的分布。为了推进，分析分发的尾巴的行为，作者也执行一个 semilog 阴谋和一个木头木头阴谋验证这个趋势是否接近泊松分布或 PL。这份报纸揭示与分数有关的行为的不同区域，与能被考虑付小费给比赛的点的时间的特定的例子。这些批评的点的存在建议有阶段转变，比赛的动态得分显著地改变。

Basketball scoring in nba games： an example of complexity

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Basketball scoring in nba games： an example of complexity