> ## Documentation Index > Fetch the complete documentation index at: https://docs.fieldfunded.com/llms.txt > Use this file to discover all available pages before exploring further. # Track NBA Player Props Line Movements > Build a Python alert system that tracks NBA player props line movements. Detect when lines shift, find value, and get notified via Discord or email. # Track NBA Player Props Line Movements with Python Player props are the fastest-moving pre-match lines in sports betting. When a star player is listed as questionable, or when sharp money hits a line, the odds shift within minutes — hours before tipoff. This tutorial builds a Python system that monitors NBA player prop lines before games start, detects significant movements, and sends you alerts so you can act before the market corrects. ## Why Player Props Move Lines move for three main reasons: | Trigger | Example | Speed | | ------------ | ------------------------------------- | ------------- | | Injury news | "LeBron listed as questionable" | Minutes | | Sharp money | High-volume bets on one side | 30-60 minutes | | Public money | Gradual movement toward popular picks | Hours | All three happen pre-match — typically 1-4 hours before tipoff. This is when monitoring is most valuable, because lines are still adjusting to new information and the market is least efficient. Pre-match prop lines offer significantly more edge and +EV opportunities than live lines, because live odds are algorithmically adjusted in real-time and leave almost no exploitable advantage. Pre-match lines, on the other hand, react slower to breaking news — giving you a window to find genuine edge before the market corrects. The FieldFunded API provides pre-match player props across all major sports, refreshed every 300ms. ## What You'll Build ```mermaid theme={null} graph LR A["Python Poller"] -->|every 60s| B["FieldFunded API"] B -->|player props| A A --> C{"Line moved?"} C -->|yes| D["Discord / Email Alert"] C -->|no| E["Store snapshot"] D --> F["CSV log for analysis"] E --> F ``` ## Prerequisites ```bash theme={null} pip install requests pandas ``` * Python 3.8+ * Free API key from [fieldfunded.com/docs](https://fieldfunded.com/docs) ## Step 1: Fetch Player Props for a Game ```python theme={null} import requests API_KEY = "your_api_key_here" BASE = "https://api.fieldfunded.com/v1" H = {"X-API-Key": API_KEY} def get_player_props(event_id): """Fetch all player prop markets for a given event.""" odds = requests.get( f"{BASE}/events/{event_id}/odds", headers=H ).json() prop_keywords = [ "player", "goalscorer", "points", "rebounds", "assists", "threes", "steals", "blocks", "passing", "rushing", "receiving" ] props = [] for market in odds.get("markets", []): name_lower = market["name"].lower() if any(kw in name_lower for kw in prop_keywords): for sel in market["selections"]: props.append({ "market": market["name"], "market_id": market["id"], "selection": sel["name"], "selection_id": sel["id"], "odds": sel["odds"], }) return props # Example: get props for a specific NBA game # props = get_player_props("event_abc123") # print(f"Found {len(props)} player prop lines") ``` ## Step 2: Find NBA Games with Props ```python theme={null} def get_nba_events(): """Get today's NBA events.""" events = requests.get( f"{BASE}/events", headers=H, params={"sport": "basketball_nba"} ).json() return events.get("events", []) # Print today's games nba = get_nba_events() for game in nba: print(f"{game['home_team']} vs {game['away_team']}") print(f" ID: {game['id']}") print(f" Kickoff: {game['commence_time']}") ``` ## Step 3: Build the Line Movement Detector ```python theme={null} import time from datetime import datetime class PropMonitor: def __init__(self, threshold_pct=3.0): self.baseline = {} # key -> first odds seen self.previous = {} # key -> last odds seen self.threshold = threshold_pct self.alerts = [] def check_event(self, event_id, event_name): """Check all player props for an event and detect movements.""" props = get_player_props(event_id) for prop in props: key = f"{event_id}_{prop['market_id']}_{prop['selection_id']}" current = prop["odds"] # Set baseline on first scan if key not in self.baseline: self.baseline[key] = current self.previous[key] = current continue prev = self.previous[key] base = self.baseline[key] # Detect movement from previous scan if prev != current: change_pct = ((current - prev) / prev) * 100 from_base_pct = ((current - base) / base) * 100 if abs(change_pct) >= self.threshold: alert = { "time": datetime.now().isoformat(), "event": event_name, "market": prop["market"], "selection": prop["selection"], "previous": prev, "current": current, "change_pct": round(change_pct, 1), "from_baseline_pct": round(from_base_pct, 1), "direction": "DRIFTED" if change_pct > 0 else "SHORTENED", } self.alerts.append(alert) self._print_alert(alert) self.previous[key] = current def _print_alert(self, a): arrow = "↑" if a["direction"] == "DRIFTED" else "↓" print( f"\n{arrow} [{a['direction']}] {a['event']}\n" f" {a['market']} — {a['selection']}\n" f" {a['previous']} → {a['current']} " f"({a['change_pct']:+.1f}% from last, " f"{a['from_baseline_pct']:+.1f}% from baseline)" ) ``` ## Step 4: Add Discord Notifications ```python theme={null} def send_discord_alert(webhook_url, alert): """Send a line movement alert to Discord.""" color = 0x53fc18 if alert["direction"] == "DRIFTED" else 0xef4444 arrow = "📈" if alert["direction"] == "DRIFTED" else "📉" embed = { "title": f"{arrow} {alert['direction']}: {alert['selection']}", "color": color, "fields": [ {"name": "Event", "value": alert["event"], "inline": False}, {"name": "Market", "value": alert["market"], "inline": True}, {"name": "Movement", "value": f"{alert['previous']} → {alert['current']}", "inline": True}, {"name": "Change", "value": f"{alert['change_pct']:+.1f}%", "inline": True}, {"name": "From Baseline", "value": f"{alert['from_baseline_pct']:+.1f}%", "inline": True}, ], "timestamp": alert["time"], } requests.post(webhook_url, json={"embeds": [embed]}) ``` Integrate it into the monitor: ```python theme={null} class PropMonitor: def __init__(self, threshold_pct=3.0, discord_webhook=None): self.baseline = {} self.previous = {} self.threshold = threshold_pct self.webhook = discord_webhook self.alerts = [] def _print_alert(self, a): # ... (same as above) # Send Discord notification if self.webhook: send_discord_alert(self.webhook, a) ``` ## Step 5: Run the Full Monitor ```python theme={null} import os DISCORD_WEBHOOK = os.environ.get("DISCORD_WEBHOOK") POLL_INTERVAL = 60 # seconds monitor = PropMonitor( threshold_pct=3.0, discord_webhook=DISCORD_WEBHOOK, ) print("=== NBA Player Props Monitor ===") print(f"Threshold: {monitor.threshold}%") print(f"Poll interval: {POLL_INTERVAL}s\n") while True: nba_events = get_nba_events() print(f"[{datetime.now().strftime('%H:%M:%S')}] " f"Scanning {len(nba_events)} NBA games...") for event in nba_events: name = f"{event['home_team']} vs {event['away_team']}" monitor.check_event(event["id"], name) # Rate limit protection time.sleep(0.3) time.sleep(POLL_INTERVAL) ``` Output: ``` === NBA Player Props Monitor === Threshold: 3.0% Poll interval: 60s [20:15:30] Scanning 6 NBA games... ↓ [SHORTENED] Lakers vs Celtics Player Points O/U — LeBron James Over 25.5 2.10 → 1.85 (-11.9% from last, -11.9% from baseline) ↑ [DRIFTED] Nuggets vs Timberwolves Player Rebounds O/U — Nikola Jokic Over 12.5 1.75 → 1.95 (+11.4% from last, +11.4% from baseline) ``` ## Step 6: Export Data for Analysis ```python theme={null} import pandas as pd def export_alerts(monitor, filename="prop_alerts.csv"): """Save all detected movements to CSV.""" if not monitor.alerts: print("No alerts to export") return df = pd.DataFrame(monitor.alerts) df.to_csv(filename, index=False) print(f"Exported {len(df)} alerts to {filename}") # Quick summary print(f"\nTop movers:") top = df.sort_values("change_pct", key=abs, ascending=False).head(5) for _, row in top.iterrows(): print(f" {row['selection']}: {row['change_pct']:+.1f}%") # Call after monitoring session # export_alerts(monitor) ``` ## Rate Limit Math | Scenario | Games | Requests/cycle | Cycles/day | Monthly | Plan | | ------------------------------ | ----- | -------------- | ---------- | ------- | ---------- | | 3 NBA games, 60s poll, 4 hours | 3 | 3 | 240 | 21,600 | Free | | Full NBA slate (8 games), 60s | 8 | 8 | 1,440 | 345,600 | Pro (\$59) | | NBA + soccer (15 events), 60s | 15 | 15 | 1,440 | 648,000 | Pro (\$59) | | Hobby: 2 games, 5-min poll | 2 | 2 | 48 | 2,880 | Free | Player props are included in the standard odds response — no additional API calls or charges. The [free tier](/compare/free-sports-api) handles casual monitoring. ## Practical Tips * Set the threshold to 3-5% for actionable alerts. Below 3% generates too much noise from normal market fluctuations. * Focus on the 2-4 hour window before tipoff. This is when injury reports drop and sharp money moves the lines most. * Pre-match props are where the edge is. Live odds are adjusted algorithmically and are extremely efficient — pre-match lines lag behind news, creating genuine +EV windows and exploitable edge that can last minutes to hours. * Player prop lines adjust faster than match winner lines to news. A "questionable" tag on a star player can move his points O/U by 10-15% within an hour. * Combine with the [settlement API](/compare/settlement-api) to automatically check if your bets on these movements won after the game. *** ## Related Guides * [Build a Player Props Tracker](/guides/build-player-props-tracker) — display player props with live odds updates * [Build a Line Tracker](/guides/build-line-tracker) — track odds movements across all market types * [Analyze Sports Data with Pandas](/blog/sports-data-analysis-python) — data analysis techniques * [Build a Discord Bot](/guides/build-discord-bot) — receive alerts as Discord slash commands * [Settlement API Overview](/compare/settlement-api) — settle player prop bets automatically Player props included on all plans — 10,000 free requests/month All plans compared side by side