Position-Specific Analytics: Bigs

The Evolving Big Man Role

No position has transformed more dramatically than center and power forward. The stretch-five revolution, pioneered by players like Dirk Nowitzki and refined by modern bigs like Nikola Jokić, has completely altered evaluation criteria. Traditional metrics like rebounds and blocks remain relevant, but rim protection efficiency, perimeter switching ability, and offensive versatility now dominate big man analysis.

Rim Protection Analytics

The most valuable defensive skill for bigs remains rim protection. Modern tracking data allows precise measurement: we track opponent field goal percentage at the rim when a big is the nearest defender, compare to expected percentage based on shot difficulty, and calculate shots altered (attempts that change trajectory or timing). Elite rim protectors like Rudy Gobert consistently hold opponents 10+ percentage points below expected at the rim.

import pandas as pd
import numpy as np

def calculate_rim_protection(defender_data):
    """Calculate comprehensive rim protection metrics"""

    rim_defense = {
        # Volume
        "rim_shots_defended": len(defender_data),
        "rim_shots_per_game": len(defender_data) / defender_data["games"].max(),

        # Efficiency
        "opp_rim_fg_pct": defender_data["shot_made"].mean(),
        "expected_rim_fg_pct": defender_data["expected_fg_pct"].mean(),
        "rim_fg_diff": (
            defender_data["shot_made"].mean() -
            defender_data["expected_fg_pct"].mean()
        ),

        # Contest quality
        "avg_contest_distance": defender_data["contest_distance"].mean(),
        "block_rate": defender_data["blocked"].mean(),
        "alter_rate": defender_data["shot_altered"].mean(),

        # Deterrence
        "deterrence_factor": 1 - (len(defender_data) / defender_data["team_rim_attempts"].sum())
    }

    # Rim protection score (composite)
    rim_defense["rim_protection_score"] = (
        -rim_defense["rim_fg_diff"] * 100 +
        rim_defense["block_rate"] * 50 +
        rim_defense["alter_rate"] * 30 +
        rim_defense["deterrence_factor"] * 20
    )

    return rim_defense

defender_tracking = pd.read_csv("rim_defense_tracking.csv")
rim_metrics = calculate_rim_protection(defender_tracking)
print(rim_metrics)

Stretch Big Evaluation

Bigs who can shoot threes at respectable rates (35%+) on meaningful volume create spacing that transforms offensive possibilities. We evaluate stretch bigs through catch-and-shoot three-point percentage, gravity metrics (how far their defender plays from the basket), and the team's rim attack rate when they're on versus off court.

# Stretch big impact analysis
library(tidyverse)

evaluate_stretch_big <- function(player_data, team_data) {
  stretch_metrics <- player_data %>%
    summarise(
      # Shooting volume and efficiency
      three_point_rate = sum(fg3a) / sum(fga),
      three_point_pct = sum(fg3m) / sum(fg3a),
      catch_shoot_3pct = sum(cs_fg3m) / sum(cs_fg3a),

      # Spacing impact
      avg_defender_distance = mean(defender_distance),

      # Usage versatility
      post_up_pct = sum(post_poss) / sum(total_poss),
      pnr_roll_pct = sum(pnr_roll_poss) / sum(total_poss),
      spot_up_pct = sum(spot_up_poss) / sum(total_poss)
    )

  # Team impact when player is on court
  on_court <- team_data %>% filter(player_on == TRUE)
  off_court <- team_data %>% filter(player_on == FALSE)

  spacing_impact <- tibble(
    team_rim_rate_on = mean(on_court$rim_fga_rate),
    team_rim_rate_off = mean(off_court$rim_fga_rate),
    rim_rate_diff = team_rim_rate_on - team_rim_rate_off,
    team_off_rtg_on = mean(on_court$off_rtg),
    team_off_rtg_off = mean(off_court$off_rtg)
  )

  list(stretch_metrics = stretch_metrics, spacing_impact = spacing_impact)
}

player <- read_csv("big_player_stats.csv")
team <- read_csv("team_on_off.csv")
analysis <- evaluate_stretch_big(player, team)

Pick-and-Roll Metrics for Bigs

The pick-and-roll remains basketball's most common action, and bigs are central to its execution. Roll men are evaluated on screen quality (slip rate, opponent separation created), rolling efficiency (points per possession as roll man), and finishing at the rim. Pop bigs add three-point efficiency to this evaluation. Defensive evaluation includes drop coverage efficiency, hedge recovery, and switch ability.

Rebounding Beyond Totals

Raw rebound totals mislead because they don't account for opportunity. Advanced rebounding metrics include contested rebound rate (percentage of rebounds where opponent was within 3 feet), box-out rate, and second-chance points prevented. Some bigs accumulate rebounds that would otherwise go to teammates; others secure difficult boards that extend or end possessions.

Post-Up Efficiency in 2024

While post-ups have declined league-wide, elite post players still generate efficient offense. Modern post evaluation includes points per possession, turnover rate, and secondary value (passes out of double teams leading to open shots). Players like Joel Embiid demonstrate that post-ups remain viable when executed by sufficiently skilled players.

Key Takeaways

• Rim protection remains the most valuable defensive skill for bigs
• Shooting ability creates spacing that benefits the entire offense
• Pick-and-roll proficiency (both ways) is essential for modern bigs
• Rebounding quality matters more than quantity
• Post-ups remain viable for truly skilled players despite league-wide decline