Search Results (78)

Football match outcomes are influenced by a complex interplay of dynamic team strategies and stochastic match events, posing a significant challenge for predictive analytics. In this paper, we present a unified benchmarking study for football match outcome classification, using a dataset of 225,474 matches spanning 2018–2026 and a strictly chronological train–test split. Six classifiers spanning several modelling approaches, Logistic Regression, Generalized Additive Models, FastTree, Random Forest, Radial Basis Function networks and Multi-Layer Perceptron, are trained and evaluated under identical conditions. Bookmaker odds are converted into margin-free probabilities by the power method, with the exponent found by the Newton–Raphson method, and are included among the input features. In addition to the six classifiers, we also report a majority-class baseline and the de-vigged bookmaker prediction itself. Results are reported for both a binary one-vs-rest formulation and the original three-class (1, X, 2) formulation, with 95% bootstrap CIs for every metric and every model. Across the six classifiers, the differences are small: macro-averaged Precision ranges from 64.83% to 65.16% for Home Win and from 68.05% to 68.46% for Away Win, and macro-averaged Recall from 63.16% to 63.51% and from 59.10% to 59.72%, respectively, with substantially overlapping confidence intervals. In the three-class formulation, models trained without market data reach 48.0% accuracy against 43.4% for a trivial baseline and 51.9% for the bookmaker. Models trained with market data match the bookmaker but do not improve upon it on log-loss, Brier score or the ranked probability score. The only statistically distinguishable improvement of any kind is a 0.086 point accuracy advantage for the additive model, which is not accompanied by any improvement in the proper scoring rules and therefore does not indicate a practical advantage. Probability calibration and betting-signal generation are outside the scope of this study. Full article

The valorisation of textile wool waste through sustainable conversion technologies such as pyrolysis has gained increasing attention as an effective strategy to reduce textile waste, recover valuable resources, and support the transition toward a circular economy. Herein, we investigated the pyrolysis of processed wool textile waste in CO₂ and N₂ atmospheres to recover valuable products and reduce the environmental impact. Key factors such as the temperature, carrier gas type, feed size, condensation set-up, and reactor configuration were evaluated for their influence on product distribution and quality. Pyrolysis at 900 °C in the presence of CO₂ led to greater gas formation (79 wt%), enhanced the stability and BET surface area of the char (10–12 wt%), and increased byproducts including phenol and indole in the bio-oil (13 wt%) product. CO made up over 65% of the gas at 900 °C due to the prevalence of the reverse (endothermic) Boudouard reaction, with the remnant gas made of CO₂ (21%) and small amounts of NH₃ (2%), HCN (0.8%) and SO₂ (0.3%). This CO-rich gas could have industrial applications such as Fischer–Tropsch after conditioning and N/S removal. Moreover, the higher carbon content (82.5% at 900 °C) increased the stability of char produced with CO₂ (compared to N₂), making it suitable for soil enhancement (~10% N at 900 °C) or pollutant removal and allowing it to be categorised and marketed as biochar. Despite low-temperature pyrolysis (350 °C) not being efficient in decomposing the whole wool waste, a staged pyrolysis with an initial low-temperature stage was shown to be effective in separately removing bromine-rich compounds. In summary, this study provides insights into the thermal decomposition behaviour of wool and the influence of the reaction conditions and reactor type on product distribution. Full article

Probabilities in sports forecasting are shaped by betting-market mechanisms, where expectations are exchanged through prices and transformed into implied probabilities. Although probabilistic predictions are usually treated as quantitative variables, rare events may be represented with lower resolution, making some probability ranges behave closer to ordinal structures. This paper investigates this hypothesis using NBA betting odds. We directly assess ordinality by examining the loss of linearity between implied probabilities and empirical outcome frequencies while evaluating whether monotonicity is preserved across probability ranges. The results show that low-probability odds deviate from the linear behavior expected under quantitative assumptions, while largely maintaining their ordinal ordering. The ordinal hypothesis is independently evaluated through a predictive experiment based on a Gated Recurrent Unit model. The model uses team-level temporal sequences as input features to predict the probability of a target game. We compare the behavior of the ordinal-aware CORAL loss across the analyzed subsets. The results suggest that the subset exhibiting ordinal characteristics attains a lower statistical risk under this ordinal inductive bias than the subset characterized by quantitative behavior. Finally, we discuss the broader applicability of comparative training with loss-function-induced inductive biases as a practical method for identifying the measurement level of target variables. Full article

This exploratory study investigates the application of the EU Taxonomy within the Romanian capital market, focusing on companies included in the BET Index and analyzing discrepancies between declarative sustainability commitments and actual technical compliance. Employing documentary content analysis and descriptive statistical design, the research introduces a convergence matrix that compares declarative intensity with taxonomic potential, mainly reflected through eligible turnover and complemented by CapEx-related indicators. The findings suggest a systemic execution gap. Despite significant eligibility in certain cases, technical alignment tends to remain very limited, mainly due to the bureaucratic and practical constraints involved in demonstrating compliance with the Do No Significant Harm criteria. Consequently, the analysis identifies a possible cross-sectional decoupling pattern: entities with low eligibility tend to compensate through extensive narrative disclosures, whereas those with higher eligibility maintain more pragmatic and technical communication. Furthermore, Capital Expenditure appears to be used as a forward-looking mechanism to project future transition efforts. The study concludes that, in emerging markets, the EU Taxonomy currently operates as a regulatory-technical proxy, emphasizing the need to distinguish substantive compliance from formal, narrative-driven disclosure. Full article

Zoomafia is frequently invoked in Italian public, advocacy, and institutional discourse to describe profit-oriented animal-related crime, but the term remains analytically broad and insufficiently connected to criminological theory. This narrative criminological review examines zoomafia as a cautious social-scientific lens for studying organized animal-related crime across heterogeneous illicit markets. Keyword-driven searches in Scopus, Web of Science, PubMed, and targeted criminological, legal, policy, and institutional sources were complemented by citation tracking and qualitative source selection. Peer-reviewed scholarship forms the analytical core, while legal, institutional, and advocacy materials are used selectively and with explicit evidentiary limits. Findings suggest that organized animal-related crime is best understood through market governance, brokerage, legal-illegal interface management, digital mediation, logistics, facilitation, evidentiary visibility, and variable convergence with other illicit economies, rather than through generic offence labels alone. The Italian perspective is analytically useful because companion-animal trafficking, dog fighting and betting circuits, clandestine horse racing, illicit slaughtering, wildlife trafficking, and online-facilitated trade can be compared within a shared frame that also exposes the limits of rhetorical mafia labelling. The article argues that zoomafia should not be treated as a self-proving mafia label, a new legal category, or a synonym for wildlife trafficking, but as a comparative framework for identifying organizational features, enforcement constraints, and evidentiary thresholds. The evidence base remains stronger on strategic recommendations than on robust comparative evaluation of enforcement effectiveness. Full article

The present study analyzes the possibilities for diversification in the iGaming sector through the integration of concepts derived from financial derivatives theory. The main idea is the development of a model introducing a mechanism for buying and selling bets between two clients as a means of early position closure—an analog to option trading in capital markets. The model is structured in three phases and four conditions, forming eight scenarios with varying probabilities and expected returns. The analysis demonstrates that, under appropriate parameters, the innovation can be potentially profitable for clients and acceptable for the bookmaker, who may offset potential losses through an increased number of registrations and an enhanced corporate image. The proposed conceptual framework provides a theoretical foundation for the creation of a secondary market in iGaming, which could lead to greater market efficiency, increased liquidity, and the rationalization of player behavior. The results emphasize the significance of an interdisciplinary approach combining game theory, behavioral economics, and financial engineering as a basis for sustainable development and competitive advantage in the dynamically evolving iGaming industry. Full article

This study introduces a fully uncertainty-aware forecasting framework for NBA games that integrates team-level performance metrics, rolling-form indicators, and spatial shot-chart embeddings. The predictive backbone is a recurrent neural network equipped with Monte Carlo dropout, yielding calibrated sequential probabilities. The model is evaluated against strong baselines including logistic regression, XGBoost, convolutional models, a GRU sequence model, and both market-only and non-market-only benchmarks. All experiments rely on strict chronological partitioning (train ≤ 2022, validation 2023, test 2024), ablation tests designed to eliminate any circularity with bookmaker odds, and cross-season robustness checks spanning 2012–2024. Predictive performance is assessed through accuracy, Brier score, log-loss, AUC, and calibration metrics (ECE/MCE), complemented by SHAP-based interpretability to verify that only pre-game information influences predictions. To quantify economic value, calibrated probabilities are fed into a frictionless betting simulator using fractional-Kelly staking, an expected-value threshold, and bootstrap-based uncertainty estimation. Empirically, the uncertainty-aware model delivers systematically better calibration than non-Bayesian baselines and benefits materially from the combination of shot-chart embeddings and recent-form features. Economic value emerges primarily in less-efficient segments of the market: The fused predictor outperforms both market-only and non-market-only variants on moneylines, while spreads and totals show limited exploitable edge, consistent with higher pricing efficiency. Sensitivity studies across Kelly multipliers, EV thresholds, odds caps, and sequence lengths confirm that the findings are robust to modelling and decision-layer perturbations. The paper contributes a reproducible, decision-focused framework linking uncertainty-aware prediction to economic outcomes, clarifying when predictive lift can be monetized in NBA markets, and outlining methodological pathways for improving robustness, calibration, and execution realism in sports forecasting. Full article

Obtaining accurate estimates of the true probabilities of sporting events remains a long-standing problem in sports analytics. In this paper we propose a new domain-driven approach that infers true probabilities from betting odds. This task is not trivial, as betting odds are noisy because of bookmaker margins (vig), insider bets, and model imperfections. In this study, we present a novel approach that integrates estimates across multiple groups of betting markets to obtain more robust estimates of true probability. Our method takes market structure into account and constructs a constrained optimisation problem that is solved using the Dixon–Coles model of a football match. We compare our approach with a wide range of existing methods, using a large dataset of 359035 matches from more than 6000 leagues. The proposed method achieves the lowest log-loss and the best probability calibration among all tested approaches. It also performs the best in terms of expected profit convergence in Monte Carlo simulations, outperforming its competitors in terms of MSE and bias. This study contributes both to a new margin-removal (devig) method and provides a comprehensive comparative analysis of other known methods. Beyond football, this approach has potential applications in other sports with discrete scoring systems and potentially in other areas involving stochastic processes and market inference, such as prediction markets, finance, reliability engineering, and social prediction systems. Full article

This study examines the emergence of cryptocurrency-enabled democracy (c-democracy) through an analysis of blockchain-based prediction markets during South Korea’s 2024 presidential impeachment crisis. Using a mixed-methods approach, namely network analysis, discourse analysis, and statistical validation, we identify transnational communities engaging in Korean politics beyond citizenship boundaries. Findings reveal a discourse–betting disconnect, where expressive, playful discourse coexists with serious financial stakes, reflecting hybrid motivations for participation. We also observe playful activism and transnational community formation that transcend geographical limits. These results highlight c-democracy as a novel form of political engagement that extends, but also complicates, traditional e-democracy frameworks. Full article

Halloumi cheese, a traditional Cypriot dairy product with Protected Designation of Origin (PDO) status, is renowned for its unique texture and high melting point. PDO certification is crucial for Halloumi cheese as it ensures the product’s authenticity, protects its traditional production methods and geographical origin, and safeguards consumers and producers against fraud and mislabeling. However, concerns over adulteration, particularly through the addition of skim milk powder, pose challenges to its authenticity and quality control. This study is the first to analyze Halloumi cheese using Brunauer–Emmett–Teller (BET) analysis and Fourier Transform Infrared (FTIR) spectroscopy, providing a novel approach to assessing its composition and authenticity. Furthermore, it marks the first time Halloumi samples have been examined in the context of PDO certification. Alongside PDO-certified Halloumi, two additional sample sets were produced following PDO specifications for moisture, fat, and salt content, with the controlled incorporation of skim milk powder as an adulterant at concentrations of 1% and 5%. Principal component analysis (PCA) was employed to visualize and interpret the spectral data, revealing promising results. Chemometric analysis showed that the specific surface area from BET measurements and the FTIR spectral subregion between 1650 and 1100 cm⁻¹ were key factors, and they were retained for model construction. These findings could play a crucial role in establishing official food fraud detection methodologies, particularly for the Cyprus and EU markets. While this study serves as an initial investigation, additional samples will be tested in future studies to validate these preliminary results and to assess the potential of applying these techniques in real-world food fraud detection scenarios. Full article

This paper analyzes the volatility relationship between the Romanian BET index and the U.S. SFT-500 index during the period 2019–2024, with a particular focus on the impact of political and geopolitical shocks. The study investigates whether financial markets in emerging economies react symmetrically or asymmetrically to external shocks originating from mature markets, especially during periods of political uncertainty. The research period includes four major systemic events: the COVID-19 pandemic, the military conflict in Ukraine, the 2024 U.S. presidential elections, and the 2024 Romanian elections, all of which generated significant volatility in global markets. The methodological approach combines time series econometrics with the Impulse Indicator Saturation (IIS) technique to identify structural breaks and outliers, without imposing exogenous assumptions about the timing of events. The econometric model includes autoregressive and lagged exogenous variables to estimate the influence of the SFT-500 index on the BET index, while IIS variables capture unanticipated political and economic shocks. Additionally, a Fractionally Integrated GARCH (FIGARCH) specification is applied to model the persistence of volatility over time, capturing the long-memory behavior often observed in emerging markets like Romania. The results confirm a statistically significant but partial synchronization between the two markets, with lagged and contemporaneous effects from the SFT-500 index on the BET index. Volatility in Romania is markedly higher and longer-lasting during domestic political episodes, confirming that local factors are a primary source of market instability. For investors, this underscores the need to embed political risk metrics into emerging market portfolios. For policymakers, it highlights how stronger institutions and transparent governance can dampen election- and crisis-related turbulence. Full article

Road freight transport is the key driver of the European economy and society; thus, distortion of its operation would have negative influence on growth and well-being. For that reason, implementation of European policies, including transport decarbonization, should be comprehensively evaluated from an environmental, social and economic perspective. In that case, introduction of electric trucks will create a mutual impact on the market and on haulage companies. The main research problem is to assess the future impact of decarbonization on the international road freight transport market structure on the supply side and the competitiveness of companies operating there. Today, a number of small and medium companies, to a great extent from Eastern Europe, render transportation services, creating a competitive structure with high flexibility, accessibility and low prices. Shifting towards electric trucks, with significantly higher upfront costs, will redefine the market structure, eliminating the small carriers and activating horizontal integration. The key objective of this research is to identify the main factors and challenges related to electric truck implementation and define crucial areas of its impact on future market structure. The research shows that the improvement of environmental performance requires low- or zero-emission trucks, where the battery technology is a leading solution. Thus, fleet renewal needs additional financial support from the public side. Different measures are available in European countries, so the level of support is not equal from a competitiveness perspective. Battery truck selling, as well as sustainable strategies, refer mostly to huge transport companies. On the other hand, the case of Polish truckers shows that the economic viability of SMEs is poor; thus, the introduction of BET would be beyond its reach. The research findings could be treated as recommendations for market regulators (EC), where the tempo of implementation, as well as availability of public support programs, should be rethinking. As a result, the costs of the transition will be covered by citizens, as customers, in the prices of products and transport service, or as taxpayers, in public support programs, mainly consumed by large market stakeholders. Full article

Financial stability analysis requires volatility modeling, especially in emerging nations where pension fund systems are very vulnerable to macrofinancial risks. In order to examine the volatility dynamics of Romania’s private pension system, this study uses daily net asset value (NAV) data from 2012 to 2024 to evaluate four GARCH-type models: standard GARCH (sGARCH), exponential GARCH (EGARCH), Glosten–Jagannathan–Runkle GARCH (GJR-GARCH), and component GARCH (C-GARCH). The analysis includes domestic and international equity indices (BET, STOXX), government bond yields (ROMGB 10Y, ROMANI 5Y), short-term interbank rates (ROBOR ON), and exchange rate fluctuations (RON/EUR). Current findings indicate that EGARCH captures asymmetric fluctuations in pension fund performance, where positive shocks generate larger increases in volatility than negative ones, highlighting an atypical asymmetry pattern. Furthermore, the stabilizing effects of government bonds are overshadowed by stock market behavior, which becomes the primary driver of risk. Fluctuations in exchange rates further increase volatility, especially in markets vulnerable to external disturbances. The findings offer empirical evidence for the necessity of more cautious risk management approaches and highlight the importance of regulatory oversight in maintaining market confidence. The study underscores the importance of customized allocation frameworks that reduce vulnerability to disruptive events while maintaining prospects for sustained growth. This new dataset contributes to enhancing the comprehension of pension fund volatility within the context of emerging markets. These insights can assist managers and policymakers seeking to fortify retirement outcomes. Full article

This paper reviews the existing studies employing total cost of ownership (TCO) analysis to evaluate the comparative economic viability of battery electric trucks (BETs) and diesel trucks (DTs). A key finding is that until recent years, BETs have not been cost-competitive with DTs. Light-duty trucks and medium-duty trucks started to become competitive in 2021 (1) according to some estimates, whereas heavy-duty trucks might remain to be not competitive even in future decades. However, (2) TCO estimates differ across continents. (3) The combing effect of fuel prices and taxes is most likely responsible for the fact that BETs enjoy a stronger competitive position relative to DTs in Europe, Asia, and Oceania, whereas, in North America, most estimates assign them poor competitiveness, both presently and in the coming years. (4) Most studies underline that significant cost disproportions persist in the heavy-duty truck segment due to its demanding operational requirements and a lack of robust high-powered charging infrastructure. Consequently, substantial financial incentives and subsidies will be required for heavy-duty trucks to enhance their economic viability, potentially accelerating cost parity from post-2035 to the near future. This paper identifies several constraints in its TCO analysis, including limited data on residual values, variability in discount rates, depreciation costs, and a lack of longitudinal and market data for BETs. Full article

The growing sports betting market works on the premise that sports are unpredictable, making it more likely to be wrong than right, as the user has to choose between win, draw, or lose. So could football, the world’s most popular sport, be predictable? This article studies this question using deep neural networks to predict the outcome of football matches using publicly available data. Data from 24,760 matches from 13 leagues over 2 to 10 years were used as input for the neural network and to generate a state-of-the-art validated feature, the pi-rating, and the parameters proposed in this work, such as relative attack, defence, and mid power. The data were pre-processed to improve the network’s interpretation and deal with missing or inconsistent data. With the validated pi-rating, data organisation methods were evaluated to find the most fitting option for this prediction system. The final network has four layers with 100, 80, 5, and 3 neurons, respectively, applying the dropout technique to reduce overfitting errors. The results showed that the most influential features are the proposed relative defending, playmaking, and midfield power, and the home team goal expectancy features, surpassing the pi-rating. Finally, the proposed model obtained an accuracy of 52.8% in 2589 matches, reaching 80.3% in specific situations. These results prove that football can be predictable and that some leagues are more predictable than others. Full article