From Manual Scanning to Automated Signals

Most serious bettors follow a process. They check the match state — the score, the minute, the momentum. They look at statistics — shots, corners, possession. They consider form, head-to-head records, and league position. When enough conditions align, they place a bet.

The problem is scale. On a busy Saturday, 50 or more matches run simultaneously across Europe's top leagues. No human can monitor all of them in real time, cross-referencing statistics against their personal criteria for every fixture. Opportunities are missed. Bets are placed late. And fatigue leads to mistakes.

ScoreSage AI's Custom Bot Builder solves this by turning your personal betting criteria into an automated scanner. You define the conditions. The system watches every live match. When your conditions are met, you get a signal — delivered to your phone via Telegram, Discord, or push notification — with all the match data you need to decide.


How the Bot System Works

1. Define Your Filters

Filters are the conditions your bot checks against every live match. You can combine filters from eight categories to create precisely the scenario you are looking for:

Category What It Covers
Match State Current score, match minute, which team is winning or losing
Match Stats Shots, shots on target, corners, possession, fouls, cards — full-time running totals
Match Stats (Half) The same statistics but filtered to a specific half — useful for first-half or second-half strategies
Pressure ScoreSage AI's proprietary pressure metrics — attacking momentum, defensive stress, match tempo
Standings League position, points, home/away form ranking
Season Stats Team-level season averages — goals scored per game, corners conceded per match, clean sheet percentage
H2H Head-to-head record over the last N meetings — win rate, goals average, corners average
Predictions ScoreSage AI's own prediction outputs — BTTS probability and over/under signals

Each filter uses an operator (greater than, less than, equals, between, etc.) and can be scoped to the home team, away team, either team, both combined, the winning team, or the losing team.

Example Filter Set

A bot designed to find second-half goals opportunities might use:

When all five conditions are true simultaneously during a live match, the bot fires a signal.

2. Set Your Win Condition

The win condition defines what counts as a "win" for your bot. This is what the system tracks to measure your bot's accuracy over time. Available win conditions include:

Each win condition has a settlement time — half-time (HT), full-time (FT), or next event (NEXT). This tells the system when to check whether the condition was met.

3. Choose Your Delivery Channel

When your bot fires a signal, you need to know about it immediately. The system supports three delivery methods:

4. Configure Your Message

Every signal message can be personalised. You control whether it shows:

This means your signals arrive with exactly the context you need — nothing more, nothing less.


Backtesting: Prove It Before You Trust It

Before running your bot on live matches, you can backtest it against historical data. The backtester runs your filter set and win condition against past matches to show you:

Backtesting is essential. A bot that fires once a month does not give you enough data to trust its hit rate. A bot that fires 200 times with a 65% hit rate is telling you something real. Use the backtester to refine your filters until you find the balance between selectivity (higher hit rate) and volume (enough signals to be useful).


Building Your First Bot: A Step-by-Step Walkthrough

Step 1: Start with a Hypothesis

Every good bot begins with a betting thesis. "I believe that when the home team is losing 0-1 at half-time but has dominated shots on target, they come back to score in the second half." That is a testable hypothesis.

Step 2: Translate to Filters

Break the hypothesis into measurable conditions:

Step 3: Set the Win Condition

The thesis predicts the home team scores in the second half, so set: Goal in Half (2nd Half, Home Team) with settlement at FT.

Step 4: Backtest

Run the backtest. If the hit rate is above 55% with a reasonable sample size (30+ signals), the hypothesis has historical support. If not, adjust the filters — perhaps tighten the shots threshold or add a possession filter.

Step 5: Go Live

Activate the bot and set up your preferred delivery channel. Monitor the first 10-20 signals manually to confirm the bot is firing in the situations you intended. Adjust if needed.


League Filtering

Not every league behaves the same way. A bot that performs well in the Premier League may not work in Ligue 1 or the Eredivisie. The Custom Bot system lets you filter by league — either include a whitelist of leagues you trust or exclude leagues where your strategy does not apply.

This is particularly useful for bots based on pressure or pace metrics, which can vary significantly across football cultures. High-pressing leagues like the Bundesliga produce different statistical profiles from more tactical leagues like Serie A.


The Leaderboard

If you are confident in your bot's performance, you can share it to the public leaderboard. This shows your bot's name, filter name, hit rate, signal count, and PnL — anonymised to protect your specific filter configuration while showcasing the results.

The leaderboard creates a competitive element and helps the community identify which types of strategies are working in the current football landscape.


Tips for Building Better Bots

1. Start Simple

Begin with two or three filters. Adding more filters increases precision but reduces signal volume. It is better to start broad and tighten than to start with 10 filters and get no signals.

2. Match Your Win Condition to Your Filters

If your filters detect high-pressure attacking situations, your win condition should relate to goals — not corners or cards. A mismatch between what your filters detect and what your win condition measures will produce unreliable results.

3. Use the Right Scope

Scoping matters. "Shots on target > 5" means something very different when scoped to the home team versus combined. Think carefully about whether your hypothesis applies to a specific team or the match as a whole.

4. Backtest Across Multiple Leagues

A strategy that works in one league may not transfer to another. Backtest across different leagues to check whether your bot's edge is universal or league-specific. If it only works in the Premier League, restrict it to the Premier League.

5. Review and Iterate

The best bots evolve. Review your bot's signals monthly. Check which wins and losses were predictable and which were random. Adjust filters to eliminate recurring false signals without overfitting to specific matches.


Key Takeaways

The Custom Bot Builder turns your betting intuition into a repeatable, testable system. Define your conditions across eight filter categories, set a clear win condition, backtest against historical data, and go live with automated delivery to Telegram, Discord, or push notifications.

Start with a simple hypothesis, prove it with backtesting, and iterate based on live results. The goal is not to build the perfect bot on day one — it is to build a framework for continuous improvement, grounded in data rather than instinct.