MaxPlay & Birdman
Birdman
Ever notice how in Valorant the map layout is basically a puzzle? I like to analyze the patterns in how people move around the spike. Think it’s worth a deeper look?
MaxPlay
Absolutely, bro! Maps are literally brain‑teasers, and every move tells a story. If you’re diving deeper, break it down by site, look at common routes, timing of plant‑and‑kill combos, and even the micro‑aggressions on the walls. Share the heat‑maps, and watch the community react—makes for epic streams! Let's crack it together.
Birdman
Sounds like a neat way to turn a map into a chess board, but if you want to crack it, I’ll need the raw data first—no heat‑maps, just the movement logs. Then we can map out the real patterns and see if the “common routes” really are the only routes. Let’s keep the analysis tight, no fluff.
MaxPlay
Sure thing, but I can’t hand over raw logs—those are locked down. What I can do is show you how to parse the replay data yourself or set up a script to pull the movement events. Let me know if you want a quick guide on that, and we’ll get right to the heat‑free analysis.
Birdman
Nice, a script sounds like the first puzzle piece. Show me the parsing bit, and I’ll see if the movement events line up with the “common routes” I’m expecting. No heat‑maps, just raw data, got it. Let's keep the code clean and the analysis sharp.
MaxPlay
Sure, here’s a clean Python snippet that pulls movement events from a Valorant replay file. It uses the `replay_parser` library (you can install it with `pip install replay_parser`). This will give you raw tick‑by‑tick positions for each player.
```python
import replay_parser
def parse_movement(replay_path, output_file):
replay = replay_parser.Replay(replay_path)
with open(output_file, 'w') as out:
out.write('player_id,tick,x,y,z\n')
for frame in replay.frames:
for player in frame.players:
pos = player.position
out.write(f'{player.steam_id},{frame.tick},{pos.x:.3f},{pos.y:.3f},{pos.z:.3f}\n')
# Example usage
parse_movement('my_replay.replay', 'movement_log.csv')
```
That writes a CSV with each tick’s position for every player. You can then load that CSV into pandas, filter for a specific map or site, and calculate travel times or common paths. No heat‑maps, just the raw movement data. Happy analyzing!
Birdman
Nice snippet, but be careful with frame rates—if you want precise timing, consider downsampling or grouping ticks to reduce noise. Also, you could hash player IDs to anonymize the data before analysis. Let me know if you need help with the pandas side.