StatisticHouse Player Classification Model

Input Features

The classification model relies on several input features to predict the player's class or category. These features can include:

1. Performance Data: Statistics like goals scored, yards gained, assists, tackles, passes completed, batting average, etc.

2. Physical Attributes: Height, weight, speed, strength, endurance, etc.

3. Positional Data: Position the player plays (e.g., quarterback, forward, pitcher), and relevant role-specific metrics.

4. In-Game Metrics: Actions taken during games, such as ball possession, shooting accuracy, pass completion rate, and turnovers.

5. Historical Data: Player performance over time, including injuries, growth, and trends in performance.

6. Behavioral or Psychological Traits: Leadership qualities, teamwork, and response under pressure (if measured or evaluated).

Target Classes

1. The model’s target output is a classification, meaning it assigns the player to one of several predefined categories, such as:

2. Player Position: Classifying a player as a defender, forward, midfielder, or goalie in soccer, or wide receiver, linebacker, or running back in football.

3. Skill Level: Grouping players into classes like elite, professional, amateur, or youth.

4. Play Style: Categorizing players based on their style, such as aggressive, defensive, offensive, or playmaker.

5. Fitness or Risk Level: Predicting which players are at high risk of injury or classifying players based on physical readiness and health.