Stats Guide

Understanding basketball statistics and metrics used throughout the dashboard. Learn how we calculate performance ratings and what each statistic means.

Key Metrics

Composite Score

What it is:

A comprehensive rating that combines multiple statistical categories into a single performance score.

Why it matters:

This metric weighs different stats (points, rebounds, assists, etc.) based on their relative importance and impact on team success. Higher scores indicate better overall performance.

Example:

A player averaging 25 points, 8 rebounds, and 6 assists might have a composite score of 65.5

Calculation:

Points weighted at 40%, rebounds at 25%, assists at 20%, and other stats at 15%

Aggregated Composite Score

What it is:

The cumulative composite score across multiple games or time periods.

Why it matters:

While composite score shows per-game performance, aggregated composite score shows total impact over a selected period.

Example:

Over 10 games with scores of 60, 65, 70, etc., the aggregated score would be 650

Calculation:

Sum of all individual game composite scores

Traditional Stats

Points Per Game

What it is:

Average points scored per game over the selected period.

Why it matters:

The primary offensive statistic, showing a player's scoring production.

Example:

28.5 PPG means the player averages 28.5 points per game

Calculation:

Total points divided by games played

Rebounds Per Game

What it is:

Average rebounds (offensive + defensive) collected per game.

Why it matters:

Measures a player's ability to secure missed shots and control the boards.

Example:

12.3 RPG indicates strong rebounding presence

Calculation:

Total rebounds divided by games played

Assists Per Game

What it is:

Average number of assists recorded per game.

Why it matters:

Shows a player's playmaking ability and willingness to create opportunities for teammates.

Example:

8.1 APG suggests excellent court vision and passing

Calculation:

Total assists divided by games played

Shooting Efficiency

Field Goal Percentage

What it is:

Percentage of field goal attempts that were successful.

Why it matters:

Basic shooting efficiency metric. Higher percentages indicate better shot selection and shooting ability.

Example:

52.5% FG means the player makes about half their shots

Calculation:

Field goals made divided by field goals attempted

Note: Minimum 5 FGA per game required for leaderboards

Free Throw Percentage

What it is:

Percentage of free throw attempts that were successful.

Why it matters:

Measures consistency from the free throw line, an uncontested scoring opportunity.

Example:

87.5% FT indicates reliable free throw shooting

Calculation:

Free throws made divided by free throws attempted

Note: Minimum 5 FTA per game required for leaderboards

3-Pointers Made

What it is:

Average number of three-point shots made per game.

Why it matters:

Shows a player's three-point shooting volume and outside scoring threat.

Example:

3.8 3PM indicates strong perimeter scoring

Calculation:

Total three-pointers made divided by games played

Advanced Analytics

Per-Minute Stats

What it is:

Statistical production scaled to per-minute basis.

Why it matters:

Allows comparison between players with different playing times by showing efficiency per minute played.

Example:

0.95 points per minute shows scoring rate regardless of total minutes

Calculation:

Total stat divided by total minutes played

Efficiency Ratios

What it is:

Ratios comparing positive and negative statistical contributions.

Why it matters:

Help evaluate overall impact by weighing productive stats against detrimental ones.

Example:

Assist-to-turnover ratio of 2.5 means 2.5 assists for every turnover

Calculation:

Positive stat divided by negative stat

Hot & Cold Streaks

What it is:

Identifies players performing significantly above (hot) or below (cold) their season average.

Why it matters:

This helps in spotting players who are currently on a performance trend, which could be valuable for fantasy basketball decisions. It compares a player's recent performance to their established baseline for the season.

Example:

A player with a season average score of 40 who is averaging 50 over their last 5 games is on a hot streak.

Calculation:

Calculated by comparing the average composite score of the last 5 games to the full season average. A difference of +/- 15% is required to qualify as a streak.

Note: Players must have at least 10 games played in the season and 5 recent games to be eligible for streak analysis.

Understanding Rankings

Minimum Games Played

What it is:

Players must have played at least 5 games to appear in leaderboards.

Why it matters:

Ensures statistical relevance and prevents small sample size anomalies.

Example:

A player with 2 games and high averages won't appear until they reach 5 games

Calculation:

Count of games with recorded statistics

Season Selection Impact

What it is:

Statistics are filtered based on the selected season.

Why it matters:

When you change seasons, all leaderboards and comparisons update to reflect that time period.

Example:

2023-24 season stats may differ significantly from 2024-25 season stats

Calculation:

All calculations limited to games within selected season

Real-Time Updates

What it is:

Statistics update as new games are played and recorded.

Why it matters:

The dashboard reflects the most current available data for ongoing seasons.

Example:

After last night's games, averages and rankings may shift

Calculation:

Continuous recalculation as new data becomes available

Similar Players Algorithm

Statistical Similarity

What it is:

Players are matched based on their overall statistical profile similarity.

Why it matters:

The algorithm compares multiple statistical categories to find players with similar performance patterns, helping identify comparable talents or playing styles.

Example:

A player averaging 25 PPG, 8 RPG, 6 APG will be matched with others having similar scoring, rebounding, and playmaking output

Calculation:

Weighted comparison across points, rebounds, assists, and composite score metrics

Similarity Score

What it is:

A percentage indicating how closely two players' statistical profiles match.

Why it matters:

Higher percentages indicate more similar players. The algorithm uses normalized statistical differences to calculate this score.

Example:

A 85% similarity means the players have very similar statistical output patterns

Calculation:

Based on the inverse of normalized statistical differences across key metrics

Note: Minimum 70% similarity required to appear in similar players recommendations

Multi-Factor Analysis

What it is:

Considers points per game, rebounds per game, assists per game, and composite scores.

Why it matters:

Rather than focusing on a single statistic, the algorithm weighs multiple performance indicators to provide a comprehensive similarity assessment.

Example:

Two players might have different scoring but similar overall impact through rebounds and assists

Calculation:

Weighted average of normalized differences: PPG (30%), RPG (25%), APG (25%), Composite Score (20%)

Need More Help?

This guide covers the fundamental statistics and metrics used in the dashboard. As we add new features and metrics, this guide will be updated accordingly.

Dashboard data is updated regularly to reflect the most current NBA statistics available.