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All Journal International Journal for Applied Information Management Journal of Current Research in Blockchain
Aljohani, Riyadh Abdulhadi M
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Exploring Football Player Salary Prediction Using Random Forest: Leveraging Player Demographics and Team Associations Aljohani, Riyadh Abdulhadi M; Alnahdi, Abdulaziz Amir
International Journal for Applied Information Management Vol. 5 No. 4 (2025): Regular Issue: December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i4.115

Abstract

This paper explores the prediction of football player salaries using a Random Forest Regressor model, leveraging player demographics and team associations as key features. The dataset consists of 684 football players, including variables such as age, nationality, position, team, weekly salary, and annual salary. The study applies exploratory data analysis (EDA) to understand the distribution of these features and identify patterns within the dataset. Data preprocessing involves handling missing values, one-hot encoding categorical variables, and splitting the dataset into training and testing sets. The Random Forest model is trained on the preprocessed data, and its performance is evaluated using common regression metrics, including R-squared (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results show that the model explains approximately 48.5% of the variance in player salaries, with an MAE of £1.92 million and an RMSE of £2.82 million. Key predictors of salary include player age, position, nationality, and team. The analysis of feature importance reveals that categorical variables such as Nation and Team have a significant impact on salary predictions. However, the model's performance is constrained by the lack of more granular data, such as player performance metrics or external economic factors. This research provides valuable insights for football team management, helping teams understand which factors contribute to salary setting and enabling more informed decisions in player recruitment and contract negotiations. It also highlights the potential for sponsorships to target players based on these predictive attributes. Future work could explore the integration of more advanced machine learning techniques and additional player data to improve predictive accuracy and model robustness.
Temporal Pattern Analysis and Transaction Volume Trends in the Ripple (XRP) Network Using Time Series Analysis Aljohani, Riyadh Abdulhadi M; Alnahdi, Abdulaziz Amir
Journal of Current Research in Blockchain Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i4.49

Abstract

This study analyzes the temporal patterns and transaction volume trends in the Ripple (XRP) network using time series analysis. The dataset comprises over 1.2 million transactions spanning three years, allowing for a comprehensive examination of long-term trends and seasonal fluctuations. Summary statistics reveal a right-skewed distribution of transaction volume, where a majority of transactions involve relatively small amounts, while a few high-value transactions contribute disproportionately to overall network activity. Time series decomposition identifies a clear upward trend in transaction volume, with notable seasonal patterns corresponding to weekly and monthly cycles. These periodic trends suggest institutional trading behaviors, liquidity management strategies, and external market influences. Comparative forecasting analysis between ARIMA and LSTM models demonstrates that LSTM achieves superior predictive accuracy, with a 30% lower Mean Absolute Error (MAE) and a 25% reduction in Root Mean Squared Error (RMSE) compared to ARIMA. These results highlight the effectiveness of deep learning in capturing non-linear transaction dynamics within the blockchain ecosystem. Furthermore, anomaly detection using Isolation Forest successfully identifies transactional irregularities, particularly during periods of high market volatility and regulatory shifts. Several anomalous transaction spikes coincide with major market events, such as sudden exchange inflows and network congestion, reinforcing the role of external factors in influencing transaction activity. These findings emphasize the need for advanced forecasting techniques and real-time anomaly detection systems to improve transaction monitoring and enhance security within blockchain networks. Future research could integrate additional on-chain metrics, off-chain factors, and alternative deep learning models to refine predictive capabilities and support more resilient blockchain analytics frameworks.
Co-Authors Alnahdi, Abdulaziz Amir