ranked order model for high school basketball analytics

truth in the data

As we ease into the offseason we took a look back at the entirety of the statistical data that we obtained last year and 3 years past. We learned a lot from traditional analytics but we also uncovered a more important model - that of rank order. In order to build out our offseason programs this year we will use interpretations from the data model to narrow the training focus. Below are a few reasons for using rank order model as well as the code snippet.

1. Customization: This model allows for the customization of how player stats are weighted based on the quality of the opponent teams. By considering the ranking of the opponent teams, it provides a more nuanced view of player performance compared to simplistic averages.

2. Contextualization: By incorporating opponent team rankings, the model contextualizes player performance within the competitive environment. It acknowledges that a player's performance against a stronger opponent might be more significant than against a weaker one.

3. Relative Performance: Unlike absolute statistics, this model evaluates player performance relative to the strength of the opposition. This relative assessment provides a clearer understanding of a player's impact, as it considers the difficulty of the opposition faced.

4. Ranking Incorporation: By integrating opponent team rankings, the model reflects the varying degrees of challenge presented by different opponents. This accounts for the fact that performance against higher-ranked teams should carry more weight in evaluating a player's effectiveness.

5. Improved Predictive Power: By balancing player stats over ranked order opponent teams, the model offers better predictive power. It provides insights into how players perform under different levels of competition, which can be invaluable for strategic planning and player development.

code snippet

import pandas as pd

import numpy as np

#DataFrame called 'player_stats'

#DataFrame called 'opponent_rankings'

def calculate_balanced_stats(player_stats, opponent_rankings):

# Assume 'player_stats' DataFrame has columns like 'points', 'rebounds', 'assists', etc.

# Assume 'opponent_rankings' DataFrame has columns like 'opponent_team', 'ranking', etc.

# Merge player stats with opponent rankings based on opponent team

merged_data = pd.merge(player_stats, opponent_rankings, left_on='opponent_team', right_on='opponent_team', how='inner')

# Calculate weighted stats based on opponent team ranking

merged_data['weighted_points'] = merged_data['points'] * (1 / merged_data['ranking'])

merged_data['weighted_rebounds'] = merged_data['rebounds'] * (1 / merged_data['ranking'])

# Add more weighted stats as needed

return merged_data

# Example usage:

balanced_stats = calculate_balanced_stats(player_stats, opponent_rankings