Data Science and ML in Cricket

 Objective of the data science in cricket  - Increate team performance  - Maximiser winning chances  Here's a simplified version: --- The IPL has expanded cricket, increasing the number of matches and the amount of data collected. Modern cricket data analysis involves tracking various factors like player positions, ball movements, shot types, delivery angle, spin, speed, and trajectory, which makes data cleaning and preprocessing more complex. **Dynamic Modeling** In cricket, numerous variables must be tracked, including player actions, ball attributes, and potential outcomes. The complexity of modeling depends on the type of predictive questions asked. Predictive models become especially challenging when analyzing hypothetical scenarios, like how a batsman’s shot might change with different ball angles or speeds. **Predictive Analytics Complexity** Cricket decision-making often relies on queries like "how often does a batsman play a specific shot against a certain b...
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Business Intelligence in Action

Business Intelligence 
Image result for business intelligence
What is Business Intelligence?
BI(Business Intelligence) is a set of processes, architectures, and technologies that convert raw data into meaningful information that drives profitable business actions

 Why BI 

  • Providing BI ready Data 
  • Data Driven decisions
  • Comparisons of multiple vendors 
  • Risk Analysis 
  • Controls on business 
  • Business Opportunities
  • Implementing an Effective Strategy
  • Competitive market advantage
  • Long-term stability
What is BI 
  • To understand the structure and the dynamics of the organization in which a system is to be deployed.
  • To understand current problems in the target organization and identify improvement potentials.
  • To ensure that the customer, end user, and developers have a common understanding of the target organization.
Data Requirement 
  • Trend Data 
  • Data files 
  • Current Data 
  • Real-time Data 
  • Department wise data 
Data Mining 
  • process of discovering patterns in large data sets 
  • methods at the intersection of machine learning
Data mining Process

  • Selection
  • Pre-processing
  • Transformation
  • Data mining
  • Interpretation/evaluation
  • Business understanding
  • Data understanding
  • Data preparation
  • Modeling
  • Evaluation
  • Deployment
Decision Making
  • Providing relation between variables 
  • Avoiding common decision making mistakes
  • Innovation Management
  • Portfolio Management
  • Business Strategy
  • Business Strategy

Process of Analytics
  • data mining,
  •  process mining
  • statistical analysis
  • predictive analytics
  • predictive modeling
  • business process modeling
  •  data lineage
  • complex event processing 
  • prescriptive analytics.
Elements of BI 
    • Multidimensional aggregation and allocation
    • Denormalization, tagging, and standardization
    • Realtime reporting with analytical alert
    • A method of interfacing with unstructured data sources
    • Group consolidation, budgeting and rolling forecasts
    • Statistical inference and probabilistic simulation
    • Key performance indicators optimization
    • Version control and process management
    • Open item management








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