Trade Value and Asset Management

Trade Value Calculation

Player trade value depends on current production, contract status, age, and fit. A player on a good contract has more trade value than an equally productive player on a bad contract. Young players typically have higher trade value than older players due to more remaining career.

Draft Pick Valuation

Draft picks have expected value based on historical outcomes at each pick position. Higher picks are substantially more valuable than later picks due to the increased probability of selecting quality players. Converting pick expected value to player value equivalent enables pick-for-player trade evaluation.

Trade Analysis

def evaluate_trade(team_a_gives, team_b_gives):
    """Evaluate trade fairness using asset values"""
    def calculate_asset_value(assets):
        total = 0
        for asset in assets:
            if asset['type'] == 'player':
                # Project remaining value
                total += asset['projected_war'] * 8_000_000 - asset['remaining_salary']
            elif asset['type'] == 'pick':
                # Expected value based on pick position
                pick_values = {1: 40, 5: 25, 10: 15, 20: 8, 30: 4}  # In millions
                total += pick_values.get(asset['position'], 3) * 1_000_000
        return total

    team_a_value = calculate_asset_value(team_b_gives)  # What A receives
    team_b_value = calculate_asset_value(team_a_gives)  # What B receives

    return {
        'team_a_receives': team_a_value,
        'team_b_receives': team_b_value,
        'advantage': 'Team A' if team_a_value > team_b_value else 'Team B'
    }

Fit and Context

Trade value isn't absolute—it depends on team context. A team with point guard depth values additional point guards less than one with a hole at the position. Optimal trade analysis accounts for roster fit, not just raw asset value.

Implementation in R

# Schedule difficulty analysis
library(tidyverse)

analyze_schedule <- function(schedule_data, team_ratings) {
  schedule_data %>%
    left_join(team_ratings, by = c("opponent_id" = "team_id")) %>%
    group_by(team_id, team_name) %>%
    summarise(
      games = n(),
      avg_opponent_rating = mean(opponent_net_rtg),
      home_games = sum(home),
      back_to_backs = sum(back_to_back),
      avg_rest_days = mean(rest_days),
      total_travel_miles = sum(travel_miles),
      .groups = "drop"
    ) %>%
    mutate(
      schedule_difficulty = scale(avg_opponent_rating)[,1] -
                            scale(home_games / games)[,1] * 0.5 +
                            scale(back_to_backs)[,1] * 0.3 +
                            scale(total_travel_miles)[,1] * 0.2
    ) %>%
    arrange(desc(schedule_difficulty))
}

schedule <- read_csv("team_schedules.csv")
ratings <- read_csv("team_ratings.csv")
schedule_analysis <- analyze_schedule(schedule, ratings)

print(schedule_analysis)

Implementation in R

# Schedule difficulty analysis
library(tidyverse)

analyze_schedule <- function(schedule_data, team_ratings) {
  schedule_data %>%
    left_join(team_ratings, by = c("opponent_id" = "team_id")) %>%
    group_by(team_id, team_name) %>%
    summarise(
      games = n(),
      avg_opponent_rating = mean(opponent_net_rtg),
      home_games = sum(home),
      back_to_backs = sum(back_to_back),
      avg_rest_days = mean(rest_days),
      total_travel_miles = sum(travel_miles),
      .groups = "drop"
    ) %>%
    mutate(
      schedule_difficulty = scale(avg_opponent_rating)[,1] -
                            scale(home_games / games)[,1] * 0.5 +
                            scale(back_to_backs)[,1] * 0.3 +
                            scale(total_travel_miles)[,1] * 0.2
    ) %>%
    arrange(desc(schedule_difficulty))
}

schedule <- read_csv("team_schedules.csv")
ratings <- read_csv("team_ratings.csv")
schedule_analysis <- analyze_schedule(schedule, ratings)

print(schedule_analysis)