Sürdürülebilir Kalkınma Amaçları
Yazarlar
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ1Match success in football: insights from raw and possession-normalized key performance indicators in the Turkish professional league
Sever, Ozan; Öncü, Çağdaş; Öztaşyonar, Yunus; Aksoy, Ömer; Bayrakdaroğlu, Serdar
Background: Match-performance indicators are widely used to explain success in football; however, traditional analyses based solely on absolute key performance indicators (KPIs) may overlook contextual factors such as ball possession and phase of play. This study aimed to examine match-performance indicators associated with winning in Turkish professional football using two complementary approaches: a raw KPI model based on absolute match actions and a possession- and phase-of-play–normalized KPI model accounting for contextual opportunity. Methodology: Match data were obtained from a validated event-based database covering six consecutive seasons, yielding 2,728 team observations from 1,364 win–loss matches. Descriptive comparisons between winning and losing teams were conducted, followed by multivariate binary logistic regression analyses to identify performance indicators independently associated with match success under both modeling approaches. Results: At the descriptive level, winning teams demonstrated higher values for shots, shots on target, accurate passes, opponent-half accurate passes, accurate crosses (%), and opponent-half accurate passes (%), whereas total possession and total passes did not differ significantly between groups. In terms of disciplinary indicators, losing teams received significantly more yellow and red cards, whereas fouls committed were significantly higher in winning teams (p = 0.014). However, the normalized indicator (fouls committed per out-of-possession) did not differ significantly between groups. In the raw KPI model, positive predictors of match success included shots on target (OR = 1.58), accurate crosses (OR = 1.10), opponent-half accurate passes (OR = 1.01), and fouls committed (OR = 1.04). Negative predictors were total crosses (OR = 0.92), yellow cards (OR = 0.89), and red cards (OR = 0.49). In the possession-normalized model, significant positive predictors were shots on target per possession, accurate crosses per possession, and fouls committed per out-of-possession. Significant negative predictors included crosses per possession, accurate crosses (%), yellow cards per out-of-possession, and red cards per out-of-possession. Conclusions: The findings indicate that combining absolute performance indicators with context-sensitive normalized metrics may provide a more comprehensive perspective on match success in football. Offensive efficiency and disciplinary control emerged as key correlates of winning, particularly when evaluated relative to possession and phase-of-play contexts. These findings suggest the potential value of integrating contextualized performance analysis into match evaluation and performance analysis frameworks.
AtıfWoS1OpenAlex1İncele - MakaleAçık Erişim2025Scopus SJRQ1Efficiency analysis using the machine learning algorithms: model development and verification
Soydaş, Şafak Sönmez; Kalkan, Yusuf; Çam, Alper Veli; Barut, Abdulkadir
Recently, machine learning (ML) algorithms have been employed intensively in the field of finance as in all sectors. The issues such as financial distress prediction, bank credit risk calculation, etc., have been analyzed using ML algorithms. This study aimed to determine firm performance with the data envelopment analysis (DEA) method, sensitivity analysis, and ML algorithms and analyze the efficiency of companies via artificial neural networks (ANNs), support vector machines (SVMs), and logistic regression (LR) classification algorithms. In the study, first, 10 financial ratios were categorized into two parts, such as output and input, and efficiency scores were determined in MS Excel software. The obtained scores were included in the ML algorithm as a categorical dependent variable. Secondly, the data were extracted and included in the analysis software as 80% training and 20% test data, and the accuracy of ML algorithms was tested. Lastly, a comparative analysis of the estimation and classification algorithms of active and inactive companies was conducted. As a result of the analysis, the best classification prediction was seen as the ANN algorithm. SVM and LR algorithms also made an acceptable level of classification prediction. It was expected that the study would have contributed to the literature in terms of testing the companies whose efficiency scores were determined by the DEA method with ML techniques and determining which technique was more successful.
AtıfScopus7İncele - MakaleYalnız künye2020Scopus SJRQ1Medium scale earthflow susceptibility modelling by remote sensing and geographical information systems based multivariate statistics approach: an example from Northeastern Turkey
Dağ, Serhat; Akgün, Aykut; Kaya, Ayberk; Alemdağ, Selçuk; Bostancı, Hasan Tahsin
The aim of the present study was to produce an earthflow susceptibility map for the city center and environs of the province of Rize located at the Northeastern part of Turkey. The study area is the rainiest region of Turkey, and due to the triggering effect of precipitation earthflows are frequently observed in and around the study area. Besides this point, weathered rock units and steep topography accompany with precipitation for the occurrence of earthflow cases. Considering this point, an earthflow susceptibility mapping was inferred to be a necessity for the area. Parameters, such as lithology, slope gradient, slope aspect, topographical wetness index (TWI), stream power index (SPI), slope curvature, and sediment capacity index (LS) were considered to be earthflow conditioning parameters. A multi-temporal earthflow inventory map for a period of 4 years (2011–2015) was initially generated by way of remote sensing approach and field surveys. Earthflow susceptibility map was produced using the logistic regression method after which the produced susceptibility map was validated. The mapped earthflows were separated into two groups prior to modelling and validation. The first group was for training and the second group was for validation steps. The accuracy of the model was measured by fitting them to a validation set of mapped earthflows. Area under curvature (AUC) approach was applied for validation purposes. The prediction capability of the earthflow susceptibility map produced can be regarded as acceptable in accordance with the AUC values of 0.62 for the logistic regression model. Based on these results, the obtained earthflow susceptibility map can be used to mitigate hazards related to landslides and to aid in land-use planning for the Rize city center.
AtıfScopus26WoS23OpenAlex31İncele