Enhancing sessions and players

Today I realised I’m simulating a “small sample”: I modified it.

From 500 sessions → 10,000 sessions.
From not identified players → 1,200 unique players.

I need real scale, a dataset that looks like what an operator actually sees weekly.

Main Goals of the day:

Increase simulation volume to 10K sessions over 7 days
Calculate a realistic number of unique players (1,200)
Ensure distribution of sessions per player is non-uniform, closer to a realistic behavior

Step by Step

📍 Step 1: Changed n_sessions = 10000 and created n_players = 1200
📍 Step 2: Calculated average sessions per player = 10000 / 1200 = 8.33
📍 Step 3: Verified that with 1,200 players, the data now has enough granularity to detect patterns:

Who plays 1 session?
Who plays 15?
Who deposits €500 in one go? Who doesn’t deposit at all?

Challenges / Insights

❌ Not “More data = better” anymore
✅ “More realistic data = better.”

I could’ve kept 500 players, and verifying their sessions, I realised:

“Every player plays exactly 2 sessions on the simulation: it’s not a simulation, it’s an Excel file”

Now?
Players have different number of sessions with a mean of 30.

Somebody tries for the first time and disappears, sometimes twice per day; somebody is more obsessed, like the real world.

Code Snippet Final

n_players = 1200
n_sessions = 10000
avg_session = n_sessions / n_players  # ~8.33 sessions per player

session_counts = np.random.poisson(lam=avg_session, size=n_players)
session_counts = np.clip(session_counts, 1, 30)  # realistic bounds

</pre>

Next Step

👉 Running the data_generator.py to generate 10K sessions, and then asking for key stats.