Sports Model Interactive
Injury Impact Models
Quantify how player absences affect team totals and prop lines. Model usage redistribution and pricing adjustments.
🩹 The Replacement Problem
📉
Production Lost
Star's points/assists/rebounds no longer contributed
🔄
Usage Redistribution
Other players get more touches, shots, minutes
📊
Net Impact
Usually 40-60% of production is replaced, 40-60% truly lost
Star Player Profile
15 35
2 12
20 35
🩹 Injury Status
✓ Full strength
All players at baseline projections
Player Projections
| Player | Usage % | Points | Assists |
|---|---|---|---|
| Star Player | 28.0% | 27.0 | 7.0 |
| Player 2 | 22.0% | 18.0 | 4.0 |
| Player 3 | 18.0% | 14.0 | 3.0 |
| Player 4 | 16.0% | 12.0 | 2.0 |
| Player 5 | 16.0% | 10.0 | 5.0 |
Team Total Impact
Full Strength
110
Adjusted Total
110.0
Impact
0.0
Team at full strength. No injury adjustment needed.
📋 Injury Type Reference
| Injury Type | Typical Duration | Line Movement | Props Impact |
|---|---|---|---|
| DNP - Rest | Out 1 game | 0.5-1 pt | Moderate |
| Ankle Sprain | Out 1-3 weeks | 2-4 pts | High |
| Concussion | Variable (protocol) | 2-3 pts | High |
| Knee Injury | Weeks to Season | 3-6 pts | Very High |
| ACL Tear | Season-ending | 4-7 pts | Extreme |
💰 Pricing Implications
Team Totals
- → Star out: Drop total by 40-60% of their scoring
- → Public overreacts—line moves too much early
- → Value often on OVER after big line drop
Player Props
- → Secondary players: Boost props (usage increase)
- → Point guards: Extra assists if scorer is out
- → Efficiency may drop (harder shots, more attention)
R Code Equivalent
# Injury impact model
calculate_injury_impact <- function(star_pts, star_usage, replacement_pct = 0.5) {
# Net points lost = star production × (1 - replacement rate)
net_loss <- star_pts * (1 - replacement_pct)
# Usage redistribution
remaining_usage <- star_usage
# Top 4 teammates get proportional boost
teammate_boosts <- c(0.35, 0.25, 0.20, 0.20) * remaining_usage
list(
team_total_adjustment = -net_loss,
teammate_usage_boosts = teammate_boosts,
replacement_rate = replacement_pct
)
}
# Example
impact <- calculate_injury_impact(27, 28, 0.5)
cat(sprintf("Team total adjustment: %.1f points\n", impact$team_total_adjustment))
cat(sprintf("Usage boost to top teammate: +%.1f%%\n", impact$teammate_usage_boosts[1]))✅ Key Takeaways
- • ~40-60% of star production is replaced
- • Team totals drop less than star's raw stats
- • Usage redistributes to remaining players
- • Public often overreacts to injury news
- • Secondary player props rise
- • Efficiency may drop (harder role)