IPL Score Prediction Using LSTM

@article{1708242,
      author = {Parshaveni Varun Teja, Gajing Parinitha, Vellula Sujit, Rame Swetha},
      title = {IPL Score Prediction Using LSTM},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {111-118},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708242.pdf},
      abstract = {Prediction of IPL scores through LSTM continues to evolve as a method which improves match forecasting accuracy. Previous methodologies relied on Linear Regression together with Decision Trees for analyzing team strength and individual performance and game conditions through variable inputs. These models demonstrate limited capability to detect sequential matches connections found within cricket matches. The paper evaluates Long Short-Term Memory (LSTM) networks as they stand against traditional approaches such as Linear Regression and Decision Trees for predicting IPL scores effectively. An evaluation of the performance metrics proves deep learning to be superior to other techniques in sports analytics analysis.},
      keywords = {LSTM, Neural Networks, Linear Regression, Decision Trees.},
      month = {May},
  }