Overview
Fantasy Basketball Analytics provides multiple ways to analyze your team’s performance. This guide explains how to interpret each type of analysis and make informed decisions about your roster.Category Rankings
How Rankings Work
Rankings are calculated by comparing all teams in your league for each statistical category:dashboard.js
Understanding Sort Direction
High vs Low Categories
High vs Low Categories
High Categories (higher is better):
- Points (PTS)
- Rebounds (REB)
- Assists (AST)
- Steals (ST)
- Blocks (BLK)
- 3-Pointers Made (3PTM)
- Field Goal % (FG%)
- Free Throw % (FT%)
- Double-Doubles (DD)
- Turnovers (TO)
Ordinal Display
Rankings are shown as ordinals (1st, 2nd, 3rd, etc.):dashboard.js
When the “Show team ranks per category” checkbox is enabled, you’ll see superscript rankings next to each stat value.
Tied Rankings
When teams have identical values, they receive the same rank:dashboard.js
Win/Loss Comparisons
The Score Column
Each row shows a win-loss record against the selected team:dashboard.js
Reading Win/Loss Records
Score format: W - L
Score format: W - L
Example: “6 - 3” means:
- You would win 6 categories against this opponent
- You would lose 3 categories against this opponent
- Ties are not counted in the record
- 6-3 = You win the matchup
- 5-4 = You win the matchup
- 4-5 = You lose the matchup
- 5-5 would be a tie (rare, as percentages usually differ)
Color Coding
Cells are color-coded to show advantages:dashboard.js
- Green/Better: Your team has the advantage in this category
- Red/Worse: The opponent has the advantage
- No color: Values are equal or missing
Category Strength Analysis
The dashboard automatically identifies your strongest and weakest categories:dashboard.js
Strength Thresholds
How categories are classified
How categories are classified
In a 12-team league:
- Strong categories: Rank 1-4 (top 30%)
- Elite production in these stats
- Build your strategy around maintaining these advantages
- Neutral categories: Rank 5-8 (middle 40%)
- Average production
- Opportunity for improvement
- Weak categories: Rank 9-12 (bottom 30%)
- Areas needing improvement
- Consider punting or targeting in trades
Analysis Display
dashboard.js
Reading Trend Charts
Weekly Progression
Trends charts plot your team’s statistics week-by-week:trends.js
Chart Interpretation
Understanding trend lines
Understanding trend lines
Upward trends (for high-better stats):
- Improving performance over time
- Recent acquisitions or player improvements
- Positive momentum
- Declining performance
- Injuries or player slumps
- Need for roster adjustments
- Consistent production
- Stable roster
- Predictable performance
- Inconsistent matchups
- Streaming players
- Injury management
Percentage Statistics
Percentage stats are formatted specially:dashboard.js
Percentage stats display with 3 decimal places (e.g., “.453” for 45.3% shooting). Small changes (±.005) can significantly impact rankings.
Trade Impact Scores
Category Impact Calculation
The trade analyzer shows how each category would change:trade_analyzer.js
Impact Display
Reading impact values
Reading impact values
Positive impacts (green):
- “+2.5” points per game
- “+.015” field goal percentage
- Indicates category improvement
- “-1.8” rebounds per game
- “-.008” free throw percentage
- Indicates category decline
- “+0.1” or “-0.1”
- Minimal effect on standings
- Neutral trade from this category’s perspective
Ranking Context
The trade analyzer shows your current rank in each category:trade_analyzer.js
Strategic Insights
The analyzer provides contextual advice:trade_analyzer.js
Percentage vs Counting Stats
Counting Statistics
Stats like points, rebounds, assists that accumulate:trade_analyzer.js
- Additive: Trading a 25 PPG player for two 15 PPG players increases points
- Volume matters: More games played = more stats
- Linear impact: Doubling minutes roughly doubles stats
Percentage Statistics
Efficiency stats calculated from makes/attempts:trade_analyzer.js
- Non-additive: Can’t simply add percentages together
- Volume affects impact: High-volume shooters have bigger influence
- Weighted by attempts: A .500 shooter on 10 FGA affects team % more than .500 on 2 FGA
Why percentage calculations are complex
Why percentage calculations are complex
When trading players that affect FG%, the impact calculation considers:
- Current team totals: Your team’s total FGM and FGA
- Players out: Subtract their FGM and FGA from your team
- Players in: Add their FGM and FGA to your team
- Recalculate: New FG% = New FGM / New FGA
Common Patterns
Punt Strategies
Identifying categories to intentionally lose:- Look for categories where you’re ranked 9th or worse
- Check if improving them would require major roster overhaul
- Consider trading away players strong in those categories
- Focus resources on competitive categories
Balanced vs Specialist Teams
Balanced teams:- Ranks 3-7 across most categories
- Few dominant strengths or glaring weaknesses
- Competitive in every matchup
- Harder to trade improve
- Ranks 1-2 in several categories
- Deliberately punt 2-3 categories
- More volatile matchup results
- Clearer trade targets
Week-to-Week Variance
Weekly results fluctuate based on:- Games played: More games = more counting stats
- Opponent quality: Strength of schedule affects all stats
- Rest days: Back-to-backs hurt efficiency
- Injuries: Even one missing star swings results
Use season totals/averages for roster decisions. Don’t overreact to a single bad week.
Advanced Analysis Techniques
Opportunity Cost
When evaluating trades, consider:Playoff Scheduling
Late-season trends matter more:- Check NBA schedules for playoff weeks
- Teams with more games during playoffs have higher value
- Back-to-backs in crucial weeks are risky
Strength of Schedule
Remaining matchups affect value:- Teams playing elite defenses will underperform
- Teams with favorable schedules are undervalued
- Consider opponent quality in trend analysis
Next Steps
Dashboard Navigation
Master the dashboard interface and controls
API Reference
Learn about the data behind the analytics