# RunsGuard Framework: Context Aware Cricket Game Strategy for Field Placement and Score Containment
## 3 Proposed Framework for Cricket Game Strategy and Field Placement Optimization
### Information Extractor
2 Processing: Equation (2) illustrates the sequential steps involved in processing as follows: \[Y=SW(FCA(FA(P(X))))\] (2) where: * \(P\) represents preprocessing. * \(FA\) denotes feature analysis. * \(FCA\) stands for feature combination analysis. * \(SW\) refers to strengths and weaknesses analysis. 1. Preprocessing * Handle missing values in the Feature Data (FeatureDataSet). * Perform data normalization to ensure consistency in Feature Data (FeatureDataSet). * Apply any necessary data transformations to enhance the dataset (FeatureDataSet). 2. Feature Analysis * Perform correlation analysis on the Feature Data to find relationships between features. * Utilize Principal Component Analysis (PCA) to identify key performance-related features. 3. Feature Combinations Analysis: * Calculate the Frequency Ratio for combinations of performance-related features using the formula: \[FR=\frac{\text{Total occurrences with a specific combination}}{\text{Total occurrences of all combinations}}\] (3) * Calculate the Performance Index for combinations of performance-related features using the formula: \[PI=\frac{\text{Total scores with a specific combination}}{\text{Total balls with a specific combination}}\] (4) (Identification of Strengths and Weaknesses Rules (SW): * Performance index (PI) based categorization * Frequency ratio (FR) based categorization 3 Output: Identified patterns of strengths and weaknesses (PatternSet), including performance indices and frequency ratios. \[Y=\{PI,FR,SW_{c},y_{1},y_{2},y_{3},\dots,y_{n}\}\] where \(SW_{c}\), \(y_{i}\) is a sequence of key performance-related features. The pivotal objective of this step is to pinpoint the strong and weak zones of the batter to strategically contain runs. To achieve this, we delve into the batsman's performance across various feature combinations, focusing particularly on how frequently a batsman plays shots in specific zones when faced with different lines, lengths, and match contexts (e.g., powerplay). This nuanced analysis is facilitated by calculating two critical metrics: the Performance Index (PI) and the Frequency Ratio (FR), which are derived from detailed ball-by-ball data. Initially, we prepared the feature data by addressing missing values, normalizing the data to ensure uniformity, and applying the necessary transformations to refine the dataset for analysis. We employed correlation analysis to explore the relationships between different gameplay features, utilizing Python's NumPy library. Principal Component Analysis (PCA), facilitated by the scikit-learn library, was then applied to identify the most significant features that influence a batsman's performance. Feature Combination Analysis involves calculating the PI and FR for specific feature combinations related to the batter. The PI measures the impact of these combinations on scoring, whereas the FR assesses how often the batsman opts for certain shots in given zones. These calculations are crucial for understanding the efficacy of the different batting strategies. Based on the PI and FR metrics, we categorized the identified patterns into the strengths and weaknesses of the batsmen. The calculated metrics, PI and FR, lay the groundwork for optimizing the fielder positions in the subsequent strategy planning step.