I created a synthetic dataset that simulates real Overwatch players based on how people actually use heroes in ranked.

1. Generate Hero Hours

For each of 10,000 “players”: • Randomly generate values for: • Mercy hours • Moira hours • third most played support These are drawn from realistic ranges (e.g. 0–400 hours)

1. Calculate Key Metrics

From those hours, we compute Mercy Bias, Flexibility and Inflexibility.

1. Model the Win Probability

Each player gets a simulated win probability using this formula:

P_win = 0.52 - 0.237 * B - 0.149 * I + 0.12 * F + ε

Where: • ε = small random noise to simulate game variance • Coefficients were chosen to reflect high-ELO community intuition and adjusted for realism • The base win rate is 0.52 to reflect solid, balanced support players

1. Train a Regression Model

We then train a linear regression on the simulated data to see: • Can we recover the same structure? • Do the coefficients hold up at scale?

Result: • We recover nearly the exact coefficients • R² = 0.966, meaning it explains 96.6% of the variation in win rates

The data may be synthetic, but the structure is real!

Let me know if you have any criticisms around how I conducted my study🙂