I need someone who has already done campaigns related to the prediction market and has at least 10k followers on Twitter or Instagram and a decent reach.. I built a bot that helps me find an edge in betting, and the goal is to create better liquidity exit through smart posts and information useful to the public.
A two month ago, I wrote about the idea behind our football bot and how we were building it from the ground up.
All testing has been completed, and we've now validated the strategy in market conditions during the FIFA World Cup.
To be fair, there weren't many trading opportunities throughout the tournament. Most matches were straightforward, with teams playing to win. The real opportunities only appeared in the third round of the group stage, when teams started calculating different qualification scenarios.
That's exactly what our bot was built for.
After two rounds had been played, we already had a very clear picture of what every team needed. Some only needed a draw, others had to win, while a few were already qualified and were openly talking about rotating their squads.
By combining media reports, coach interviews, press conferences, and the tournament standings, the bot was able to identify these situations long before the betting markets fully adjusted.
Liquidity wasn't an issue at all. We comfortably entered positions of around €10,000 per match, and the markets absorbed them without any problem. Looking back, my only regret is that, since this was still the testing phase, I didn't trade with larger stakes.
These were every trade the bot executed during the tournament:
| Match | Profit |
|---|---|
| Bosnia & Herzegovina – Qatar (Home win) | +€2,142.86 |
| Paraguay – Australia (Draw) | +€3,750.00 |
| Egypt – Iran (Draw) | +€2,307.69 |
| Croatia – Ghana (Draw) | +€1,250.00 |
| Algeria – Austria (Draw) | +€4,761.90 |
| Norway – France (Away win) | +€2,692.31 |
| Total Profit (before Betfair commission) | €16,904.76 |
Just six trades generated €16,904.76 in profit
Bosnia win we catch 1.70 ( open 1.80+ and on some place ) but still good catch and we close on 1.40 win
Paraguay match we catch 3.30 and closed around 2.40
Egypt vs Iran 3.20 we have and sell on 2.60
Croatia Ghana we buy 3.60 sell to 3.20 ( even I expected bigger profit and odds bellow 3.00 but didnt happen)
Algeria - Austria we catch 3.10 sell on 2.10
When France win we have 1.65 win sell on 1.30 ( some bookies even have bigger odds)
As you can see in the table above, every position was entered before the market reacted and exited at significantly shorter odds. We never held a position until kickoff. Every trade was closed before the match started, meaning the result of the game was completely irrelevant to our profit.
That's the entire philosophy behind the bot.
We're not trying to predict football matches. We're predicting how the market will react once new information becomes widely known.
This is what I believe is the future of sports trading. Instead of waiting for services like OddsJam or other arbitrage scanners to alert you after an opportunity has already appeared, the goal is to be one step ahead.
The bot continuously monitors:
By combining all of that information, it identifies situations where the market is likely to move before everyone else notices.
The objective has never been to gamble or take unnecessary risks. The objective is to protect capital first, react before the crowd, and create arbitrage opportunities before the market fully prices them in.
With all the major domestic football leagues about to begin, we'll have hundreds of matches every week instead of just a handful from the Club World Cup.
That means far more opportunities than we saw during testing.
The FIFA World Cup proved one thing: the concept works.
Now it's time to scale it
Quick background: I've successfully built an automated basketball bot that consistently generates profit, and I'm currently in the final stages of building the same for football. The natural question came up - which sport is next?
I considered handball and volleyball because insider information is relatively easy to come by, but the fundamental problem is the low volume of those leagues. It's difficult for investors who want to play long-term, and Exchange, Kalshi and similar platforms don't offer those sports in any meaningful way.
Why cricket?
The volume is excellent, betting popularity is huge (especially the Asian market), and odds are available on all major platforms. On paper - an ideal candidate.
The problem:
I have zero knowledge of cricket. Without understanding the sport there's no value betting, and without value betting there's no model worth building. I don't have anyone in my circle with relevant knowledge and experience.
What I'm looking for:
Someone who:
The idea is a test phase - we see together if we have an edge, build the model, and if the results confirm the premise, we scale just like we do in basketball.
If you're that person or know someone like that - DM me.
Quick background: I've successfully built an automated basketball bot that consistently generates profit, and I'm currently in the final stages of building the same for football. The natural question came up - which sport is next?
I considered handball and volleyball because insider information is relatively easy to come by, but the fundamental problem is the low volume of those leagues. It's difficult for investors who want to play long-term, and Exchange, Kalshi and similar platforms don't offer those sports in any meaningful way.
Why cricket?
The volume is excellent, betting popularity is huge (especially the Asian market), and odds are available on all major platforms. On paper - an ideal candidate.
The problem:
I have zero knowledge of cricket. Without understanding the sport there's no value betting, and without value betting there's no model worth building. I don't have anyone in my circle with relevant knowledge and experience.
What I'm looking for:
Someone who:
The idea is a test phase - we see together if we have an edge, build the model, and if the results confirm the premise, we scale just like we do in basketball.
If you're that person or know someone like that - DM me.
Quick background: I've successfully built an automated basketball bot that consistently generates profit, and I'm currently in the final stages of building the same for football. The natural question came up - which sport is next?
I considered handball and volleyball because insider information is relatively easy to come by, but the fundamental problem is the low volume of those leagues. It's difficult for investors who want to play long-term, and Exchange, Kalshi and similar platforms don't offer those sports in any meaningful way.
Why cricket?
The volume is excellent, betting popularity is huge (especially the Asian market), and odds are available on all major platforms. On paper - an ideal candidate.
The problem:
I have zero knowledge of cricket. Without understanding the sport there's no value betting, and without value betting there's no model worth building. I don't have anyone in my circle with relevant knowledge and experience.
What I'm looking for:
Someone who:
The idea is a test phase - we see together if we have an edge, build the model, and if the results confirm the premise, we scale just like we do in basketball.
If you're that person or know someone like that - DM me.
Hey, throwing this out there to see if anyone's interested.
Been working on a AI soccer betting project for a while now it's basically built around getting ahead of the market through team news, lineup rotations, injury updates, motivation factors, that kind of thing. Not your typical "model spits out picks" setup. It's more about timing and information flow.
Almost done putting it together and I'm looking for a few people in the US who have real access to the main books : FanDuel, DraftKings, Caesars, BetMGM, Kalshi, Polymarket, etc and can actually move decent size.
Quick example from tonight: Blooming vs Bolívar. Got in on Home Win at 4.30. Market's already sitting around 2.83–3.00. That's the whole idea position early, before the public catches on and the line moves.
I already run something similar for basketball but that one's more value-oriented. This football project is different, it's almost entirely reaction-based and time-sensitive, so execution speed actually matters.
What I'm looking for is pretty straightforward: US sportsbook access with solid limit, Ability to act fast when needed, Someone who's been around betting markets long enough to not need hand-holding.
If that sounds like you, drop a comment or DM me
I already wrote about my process of building a basketball AI bot, and now it's time for a football one. (Full story about basketball you can read here: https://www.reddit.com/r/AI_Betting/
Why we can't just copy the basketball approach for football
The obvious move would be to take the same system, point it at football, and run the same injury tracking playbook. We thought about doing exactly that. Then we thought harder and realised it wouldn't work nearly as well.
Here's the difference. In basketball you have five players on the court. One great player can be 20 to 30 percent of the team's entire offensive output. When he's out, the market has to reprice dramatically. In football you have eleven players, and even the best team in the world has enough depth that one absence, unless it's truly one of the top five players in the world, often doesn't move things enough to make a clean trade.
There are exceptions obviously. But as a reliable systematic edge, injury tracking in football just doesn't have the same signal quality as in basketball. We tested it. The numbers weren't convincing enough.
So we asked a different question. What does move the market in football, and what do most people consistently underestimate or miss entirely?
The answer is motivation. Or more precisely, the lack of it.
The football edge: motivation and rotation
Football at the top level is played across multiple competitions simultaneously. A club like Bayern Munich or Arsenal might be playing league games, a domestic cup, and the Champions League all in the same month. Managers don't have unlimited energy in their squad. They have to make decisions about which games get the first team and which games get a rotated or rested lineup.
When a team has a huge game coming up in three days, say a Champions League semi-final, and they have a league fixture against a mid-table side the weekend before, the manager very often uses that league game to rest his key players. Everyone who follows football knows this happens. The market, however, doesn't always price it in correctly, especially early in the week before the lineup is officially confirmed.
And here's the thing. Managers tell you in advance. They say it in press conferences. They say things like "we need to be careful with our players this week" or "we have a big game on Tuesday so Saturday is a chance to give some others minutes" or sometimes they just come straight out and say they're rotating the squad. This information is public. It's in the transcript of a press conference that happened yesterday afternoon. But it takes time for that to filter into how the market prices the game.
Our football bot is built specifically to catch that window.
What the bot actually monitors
There are three main things we're tracking, and they all feed into the same question: is this team going to play at full strength, and does the market know that yet?
Coach press conference statements
Every major European league requires pre-match press conferences. The bot processes these transcripts immediately after they're published, looks for anything related to squad rotation, player rest, managing workload, or prioritising other competitions. It's trained to catch both direct statements and the kind of vague coded language managers use when they don't want to fully give away the lineup.
Fixture congestion and competition context
The bot maps out every team's full schedule across all competitions and flags matches where the surrounding context suggests rotation is likely. A team playing three games in seven days, the middle one being a cup final, is almost certainly going to treat the other two differently. We're scoring each upcoming fixture based on how much it matters relative to what else is happening for that club.
Historical rotation patterns per manager
Some managers rotate heavily. Some almost never do. Some only rotate when they're already safe in the league but will run the first team into the ground if there's still something to play for. We build profiles of individual managers based on their historical behaviour so that when a press conference statement comes in, we can calibrate how seriously to take it based on what that specific manager actually does in practice.
The Bayern example this is what it looks like in practice
During our testing phase the bot flagged the Bayern Munich vs Heidenheim Bundesliga match. Bayern had their Champions League semi-final against PSG coming up a few days later. The manager gave a press conference where he confirmed the club would be protecting key players for the bigger game.
At the time the bot flagged this, the odds on Heidenheim winning away were sitting at 19. That's a massive number. It reflected the market's assumption that Bayern would field a strong team. Once you factor in that Bayern were going to rest half their squad, those odds should be nowhere near 19.
After the information spread and the market adjusted, the Heidenheim away win odds dropped to 9. From 19 to 9. If you were positioned at 19 and you exited at 9 you made a huge return with basically zero dependency on the actual result. You're not betting on Heidenheim to win. You're betting that the market will move in a direction you already know it has to move in.
That same week the bot found seven other matches with similar profiles. Seven in one week where teams were clearly managing their squads for upcoming bigger fixtures and the market hadn't fully adjusted yet.
Where the money actually goes: Polymarket, Kalshi, Betfair
We're not placing bets in the traditional sense. We're trading positions on markets that price football match outcomes, and we're exiting those positions before the match starts once the market has corrected.
Polymarket and Kalshi are prediction market platforms where you can take positions on event outcomes. Betfair Exchange is a peer-to-peer betting exchange where you're trading against other bettors rather than against a bookmaker. On an exchange you can back an outcome to happen but also lay it, meaning you can profit from it not happening. This gives you much more flexibility to enter and exit positions based on price movement rather than just waiting for the event outcome.
The ideal scenario on all three is the same. You get in early when the information isn't priced in yet. The market corrects over the hours or days before the match. You exit at the adjusted price and take the difference. The match kicks off and it's irrelevant to you.
Honest update on where the football bot is right now
I want to be straight with you. The football bot is not fully operational yet. The core logic works and the test results are genuinely exciting but there's still roughly a month of work before it's ready for proper deployment.
The main things still being built out are coverage and calibration. Coverage means making sure we're catching press conferences and relevant news across all the major leagues we want to monitor, in multiple languages, without missing anything. Calibration means making sure the confidence scoring is accurate enough that when the bot says something is a high-quality signal it actually is, and we're not chasing noise.
We're also backtesting everything we can. Five seasons of Bundesliga, Premier League, La Liga, Serie A and Ligue 1 data run through the logic to see how it would have performed historically. Those results are looking solid but I'll share the actual numbers when we're closer to launch rather than give you half-baked figures now.
The approach is right. The edge is real. We're just being careful not to rush it out before it's ready. The basketball bot taught us that building slowly and testing everything before going live is worth the patience.
More updates coming as we get closer. Happy to answer questions in the comments about any of this.
I already wrote about my process of building a basketball AI bot, and now it's time for a football one. (Full story about basketball you can read here: https://www.reddit.com/r/AI_Betting/
Why we can't just copy the basketball approach for football
The obvious move would be to take the same system, point it at football, and run the same injury tracking playbook. We thought about doing exactly that. Then we thought harder and realised it wouldn't work nearly as well.
Here's the difference. In basketball you have five players on the court. One great player can be 20 to 30 percent of the team's entire offensive output. When he's out, the market has to reprice dramatically. In football you have eleven players, and even the best team in the world has enough depth that one absence, unless it's truly one of the top five players in the world, often doesn't move things enough to make a clean trade.
There are exceptions obviously. But as a reliable systematic edge, injury tracking in football just doesn't have the same signal quality as in basketball. We tested it. The numbers weren't convincing enough.
So we asked a different question. What does move the market in football, and what do most people consistently underestimate or miss entirely?
The answer is motivation. Or more precisely, the lack of it.
The football edge: motivation and rotation
Football at the top level is played across multiple competitions simultaneously. A club like Bayern Munich or Arsenal might be playing league games, a domestic cup, and the Champions League all in the same month. Managers don't have unlimited energy in their squad. They have to make decisions about which games get the first team and which games get a rotated or rested lineup.
When a team has a huge game coming up in three days, say a Champions League semi-final, and they have a league fixture against a mid-table side the weekend before, the manager very often uses that league game to rest his key players. Everyone who follows football knows this happens. The market, however, doesn't always price it in correctly, especially early in the week before the lineup is officially confirmed.
And here's the thing. Managers tell you in advance. They say it in press conferences. They say things like "we need to be careful with our players this week" or "we have a big game on Tuesday so Saturday is a chance to give some others minutes" or sometimes they just come straight out and say they're rotating the squad. This information is public. It's in the transcript of a press conference that happened yesterday afternoon. But it takes time for that to filter into how the market prices the game.
Our football bot is built specifically to catch that window.
What the bot actually monitors
There are three main things we're tracking, and they all feed into the same question: is this team going to play at full strength, and does the market know that yet?
Coach press conference statements
Every major European league requires pre-match press conferences. The bot processes these transcripts immediately after they're published, looks for anything related to squad rotation, player rest, managing workload, or prioritising other competitions. It's trained to catch both direct statements and the kind of vague coded language managers use when they don't want to fully give away the lineup.
Fixture congestion and competition context
The bot maps out every team's full schedule across all competitions and flags matches where the surrounding context suggests rotation is likely. A team playing three games in seven days, the middle one being a cup final, is almost certainly going to treat the other two differently. We're scoring each upcoming fixture based on how much it matters relative to what else is happening for that club.
Historical rotation patterns per manager
Some managers rotate heavily. Some almost never do. Some only rotate when they're already safe in the league but will run the first team into the ground if there's still something to play for. We build profiles of individual managers based on their historical behaviour so that when a press conference statement comes in, we can calibrate how seriously to take it based on what that specific manager actually does in practice.
The Bayern example this is what it looks like in practice
During our testing phase the bot flagged the Bayern Munich vs Heidenheim Bundesliga match. Bayern had their Champions League semi-final against PSG coming up a few days later. The manager gave a press conference where he confirmed the club would be protecting key players for the bigger game.
At the time the bot flagged this, the odds on Heidenheim winning away were sitting at 19. That's a massive number. It reflected the market's assumption that Bayern would field a strong team. Once you factor in that Bayern were going to rest half their squad, those odds should be nowhere near 19.
After the information spread and the market adjusted, the Heidenheim away win odds dropped to 9. From 19 to 9. If you were positioned at 19 and you exited at 9 you made a huge return with basically zero dependency on the actual result. You're not betting on Heidenheim to win. You're betting that the market will move in a direction you already know it has to move in.
That same week the bot found seven other matches with similar profiles. Seven in one week where teams were clearly managing their squads for upcoming bigger fixtures and the market hadn't fully adjusted yet.
Where the money actually goes: Polymarket, Kalshi, Betfair
We're not placing bets in the traditional sense. We're trading positions on markets that price football match outcomes, and we're exiting those positions before the match starts once the market has corrected.
Polymarket and Kalshi are prediction market platforms where you can take positions on event outcomes. Betfair Exchange is a peer-to-peer betting exchange where you're trading against other bettors rather than against a bookmaker. On an exchange you can back an outcome to happen but also lay it, meaning you can profit from it not happening. This gives you much more flexibility to enter and exit positions based on price movement rather than just waiting for the event outcome.
The ideal scenario on all three is the same. You get in early when the information isn't priced in yet. The market corrects over the hours or days before the match. You exit at the adjusted price and take the difference. The match kicks off and it's irrelevant to you.
Honest update on where the football bot is right now
I want to be straight with you. The football bot is not fully operational yet. The core logic works and the test results are genuinely exciting but there's still roughly a month of work before it's ready for proper deployment.
The main things still being built out are coverage and calibration. Coverage means making sure we're catching press conferences and relevant news across all the major leagues we want to monitor, in multiple languages, without missing anything. Calibration means making sure the confidence scoring is accurate enough that when the bot says something is a high-quality signal it actually is, and we're not chasing noise.
We're also backtesting everything we can. Five seasons of Bundesliga, Premier League, La Liga, Serie A and Ligue 1 data run through the logic to see how it would have performed historically. Those results are looking solid but I'll share the actual numbers when we're closer to launch rather than give you half-baked figures now.
The approach is right. The edge is real. We're just being careful not to rush it out before it's ready. The basketball bot taught us that building slowly and testing everything before going live is worth the patience.
More updates coming as we get closer. Happy to answer questions in the comments about any of this.