Rebecca Carstens, Raj Deshpande, Pau Esteve, Nicolò Fidelibus, Sara Linde Neven, Ramona Ottow, Lokamruth K. R., Paula Rodríguez-Sánchez, Luca Santagata, Javier M. Buldú, Brennan Klein, Maddalena Torricelli

Elite football is believed to have evolved in recent years, but systematic evidence for the pace and form of that change is sparse. Drawing on event-level records for 13,067 matches in ten top-tier men’s and women’s leagues in England, Spain, Germany, Italy, and the United States (2020-2025), we quantify match dynamics with two views: conventional performance statistics and pitch-passing networks that track ball movement among a grid of pitch (field) regions. Between 2020 and 2025, average passing volume, pass accuracy, and the percent of passes made under pressure all rose. In general, the largest year-on-year changes occurred in women’s competitions. Network measures offer alternative but complementary perspectives on the changing gameplay in recent years, normalized outreach in the pitch passing networks decreased, while the average shortest path lengths increased, indicating a wider ball circulation. Together, these indicators point to a sustained intensification of collective play across contemporary professional football.

Read the full article at: arxiv.org

Carlos Mendoza Montano

Front. Complex Syst., 31 July 2025

Traditional evolutionary theory explains adaptation and diversification through random mutation and natural selection. While effective in accounting for trait variation and fitness optimization, this framework provides limited insight into the physical principles underlying the spontaneous emergence of complex, ordered systems. A complementary theory is proposed: that evolution is fundamentally driven by the reduction of informational entropy. Grounded in non-equilibrium thermodynamics, systems theory, and information theory, this perspective posits that living systems emerge as self-organizing structures that reduce internal uncertainty by extracting and compressing meaningful information from environmental noise. These systems increase in complexity by dissipating energy and exporting entropy, while constructing coherent, predictive internal architectures, fully in accordance with the second law of thermodynamics. Informational entropy reduction is conceptualized as operating in synergy with Darwinian mechanisms. It generates the structural and informational complexity upon which natural selection acts, whereas mutation and selection refine and stabilize those configurations that most effectively manage energy and information. This framework extends previous thermodynamic models by identifying informational coherence, not energy efficiency, as the primary evolutionary driver. Recently formalized metrics, Information Entropy Gradient (IEG), Entropy Reduction Rate (ERR), Compression Efficiency (CE), Normalized Information Compression Ratio (NICR), and Structural Entropy Reduction (SER), provide testable tools to evaluate entropy-reducing dynamics across biological and artificial systems. Empirical support is drawn from diverse domains, including autocatalytic networks in prebiotic chemistry, genome streamlining in microbial evolution, predictive coding in neural systems, and ecosystem-level energy-information coupling. Together, these examples demonstrate that informational entropy reduction is a pervasive, measurable feature of evolving systems. While this article presents a theoretical perspective rather than empirical results, it offers a unifying explanation for major evolutionary transitions, the emergence of cognition and consciousness, the rise of artificial intelligence, and the potential universality of life. By embedding evolution within general physical laws that couple energy dissipation to informational compression, this framework provides a generative foundation for interdisciplinary research on the origin and trajectory of complexity.

Read the full article at: www.frontiersin.org

Tom Grad , Christoph Riedl , Gavin J. Kilduff

Management Science

Existing rivalry research finds that people try harder and perform better when competing against their rivals. However, are there conditions under which rivalry can harm performance? We integrate rivalry theory with regulatory fit theory to propose two moderators of rivalry: individual skill and situational risk for status change. We test our predictions using data from software programming contests involving more than 4.6 million competitive encounters across 16,846 software developers (“coders”) to examine the causal effects of rivalry and the conditions under which it may backfire. We find that, on average, coders who are randomly assigned to compete against a field of competitors with whom they share a rivalrous history exhibit higher performance, above and beyond other established drivers of performance in competition. Importantly, however, this positive effect of rivalry is moderated by (1) coders’ skill level, such that rivalry is more beneficial for more skilled coders and is harmful for less skilled coders, and (2) coders’ risk of experiencing a status change, such that coders who face a possible status loss exhibit decreased performance when competing against rivals. Thus, we extend research on rivalry by revealing the conditions under which it can harm performance, which is vital to understanding its role in organizations.

https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2023.00344