12.05.2024

Hello world it's siraj and I built a. sports betting bot with chat GPT and in. this app called GPT wager you can see. that I've made two bets the first one is. about a thousand US dollars worth on the. Golden State Warriors the second one is. about a thousand dollars on the Brooklyn. Nets and this is because of my bot it's. because of the predictions that it. output and in this video I'm going to. show you how I built this bot what the. results are at the end you'll see if I. lost two thousand dollars or I made up. to as you can see a combined total of. about ten thousand dollars that's at the. end of the video so stay tuned but. before we get there let's build it. together with chat GPT let me show you. how I built it and we're the most. important part of this video and what I. really want to stress to you is.

Mathematics and how awesome mathematics. is mathematics helps you make money and. in this video we're going to start with. a very simple bot it's an Arbitrage bot. I'll explain what that is then we'll. improve it to be what's called an XG. boost spot we'll improve it again to be. a deep learning bot then we'll add deep. learning plus sentiment analysis on. Twitter so we can see what people are. saying on Twitter about a team and use. that to improve our sports betting model. all right so that's what we're going to. do in this video so the first step for. us as we build this with chat GPT the. first step for us is going to be to ask. it a question that question is going to. be show me a list of the top 10 most. common math techniques now remember we. love maths we're going to ask you about. math techniques and we're going to be.

Very direct to make money from sports. betting you know I've heard terms like. Arbitrage and let's let's give it some. context here remember chat GPT remembers. context so I've heard terms like. Arbitrage and expected value betting I'm. not sure if that's helpful or related. just like we would talk to a human. period let's just see if it's going to. help us now I have to relog in because. it's been a while that's always I know. annoying to have to do that let me paste. that back in here. very real stuff that's how it. that's how it is with chatgpt so it's. going to list a bunch of different. techniques and all of these techniques. are going to be in the categories of. math like probability that's about. likelihood and whether or not something. will happen that's what it's concerned. with then we have statistics and that's.

About empirical data usage it's a. collection of tools to analyze data and. then we have algebra arithmetic right. numbers plus minus subtraction Division. and even calculus in the case of Markov. chain Monte Carlo that's a way of. simulating different outcomes and we can. use calculus to find the rate of change. or the derivative of different variables. so we can see 10 methods right off the. bat that chat GPT gave us to make money. from sports betting and we don't know. what any of these terms are yet because. we're noobs I mean we know a few terms. but we're going to pick one of them. we're just going to pick number two. Arbitrage betting and that's going to be. the first one that we're going to pick. and what is arbitrage betting Okay so. Arbitrage is this idea of in the sports. betting space we have all of these.

Different sports books and sports books. are always betting on the odds of. different results whether one team wins. or one team loses whether a certain. player is going to do well or not all. these things are odds and they use. quantitative models to predict these. odds and they're really good at this. what Arbitrage betting is is it saying. that hey if I bet on the on all possible. outcomes across a variety of sports. books because they all have different. odds for the same outcomes I can find. these inefficiencies in this market. because it's very similar to a financial. like a stock market it's like a sports. betting Market I can find very similar. inefficiencies and then I can exploit. them to make money and so the if the sum. of the inverse of all of the. probabilities of the odds of a given. game are less than one we can say that.

An Arbitrage opportunity exists so even. if we make two bets in two different. directions. if there is a real Arbitrage opportunity. we can be guaranteed a return but that. return isn't going to be that big it's. going to be between one to ten percent. max and the sports folks are going to. get to get wind of what we're trying to. do and they're probably going to ban us. so it's not the best technique probably. but it's a good place to start so let's. ask it to do that let's. um ask it to. build us a simple Arbitrage bot so show. me an example. of an Arbitrage bot. in Python for sports betting and have it. be real simple have it be super simple. and fit into a single class file because. we don't like giant projects with many. dependencies and we're going to be very. specific we're going to say it uses. mathematics to output and we're going to.

Be very bold as well a provably. profitable strategy okay and then we're. going to be very. needy with it then explain the math. behind it to me. okay. and hopefully it gives us a working. example and it did before I swear to you. but right now it's decided that this is. um not what it wants to do but. it might let's wait for chat GPT please. chat GPT do this for us we need this to. happen. um it can give us an example thank you. ah show me a python Arbitrage bot for. sports betting simple example that fits. into one class file I think adding the. math thing it didn't like that so given. that using a single library and let's. say three different book markers we can. do you know a threeway Arbitrage as. well it's going to find that Arbitrage. so let's take this code and let's go to. a Google collab notebook. collab.research.google.com we'll open.

That notebook it's just an easy way to. run python code even if you're not like. a super good code or anything so we'll. paste that right in there and we'll run. that and we can see that there's already. an issue with this and the issue is that. this. api.bookmarker1.com is not legit so. we've got to get some legit Sports data. so let's ask it for some of that so. that's going to be our next question. going back to our original prompt series. here and we're going to say show me. let's go back here show me a list. of the top 10 open source odds apis for. sports betting we don't just want we. want several. and I have gone through some of these. and it's it can be quite a pain to find. a developer API given whatever area of. the world you're living in so. um in the end the one that I found that. would work is the odds API and here it.

Is odds API right here so that's the one. we're going to use the odds API use the. odds API. uh in Python to pull NBA data. pull NBA data. and then it's going to give that to us. and then what we're going to do is we're. going to sign up for the odds API. because we need that and here it is odds. API we can see we need to get an API key. it's going to start out free perfect. enter our name and everything assume. we've signed up for that and once we've. signed up for that we're going to go. back to. the main page and we're going to get. that API key where is it. it's under account here's our API key. okay and what we can do is we can go to. sheets and it's got this Google Sheets. integration where it can just pull that. from our Google sheet so let's start. with that we'll start with a simple. Google sheet we'll make it a new Google.

Sheet now we've assume we've installed. this addon which I've already done and. once we have that addon we can go to. extensions sports odds start and it's. going to pull up a live odds API we can. paste in our API key that it gave us. back here under account. and then we're going to populate this. Excel spreadsheet with all of the NBA. sports we're going to pick NBA from the. list here basketball and then we want it. to be in decimal versus American. and then we'll fetch it okay so here it. is. we've got latest the latest data right. here from different bookmarkers like. bookmakers like DraftKings and Bovada. and all this stuff and what we can do is. we can find the we can Arbitrage the. odds and basically compute what that. profit is going to be so using this odds. API I wonder if they have some simple. python examples for us hopefully they do.

So we'll go to home we'll go to code. samples and then boom they've got some. python examples here running on replit. we'll show the files it's going to be a. main.pi file we'll just take the entire. thing here. and we'll copy it go back to our. code here. and that compiled it and now we have. those in python as well so now what we. have to do is we have to compile this. data that we pulled using the odds API. with that Arbitrage bot that chadbt give. has given us and that's going to require. data cleaning we're gonna have to take. that API clean the data and then process. it insert it into these two definitions. this is going to take some time and. energy now let's we can do that but. before we do that let's just do. something really quickly just to make. sure that we save enough time let's just. go to GitHub real quick and we're just.

Going to search a single search term. that's all we're just going to search. for statistical. Arbitrage. for sports betting just to make sure. nobody's done this be oh there's one. right there by Ryan crewman's knocker. thank you Ryan for this and it was made. four months ago very cool it's using the. odds API okay perfect uh that's exactly. what we need to do what Arbitrage okay. we didn't even have to do any of this. work this guy's already done it for us. and that is the value of getting good at. searching for code on GitHub because. there's so much value to be found there. so let's run this thing this guy's got. an IPython notebook for us and it's. going to create an Excel spreadsheet. just like we found before the odds API. it's going to get all that and then wow. that's a lot of data once it's got that. data what's going to happen next look at.

All of this parsing that it's going to. do find the number of possible outcomes. find the best odds determine the odds. and then you know extract the each. individual bookmaker we would have to. write all these functions ourself we. don't want to do that so let's go back. here and we're going to download this. and upload this to Google collab so. first step go to download zip we clicked. on download save the file it's saved now. we open the zip and we're going to. upload it to Google collab so we'll go. to colab.research.google we'll go to. upload and then we're going to choose. that and upload it to uh Google collab. but I've already uploaded it and it's. right here so. um we can go through this and run this. ourselves so once we install this pip uh. repository then we can just go right. ahead and start compiling this code and.

See what this Excel spreadsheet that it. gives us is going to be so we've. compiled that we've got the odds thank. you now we're going to go through his uh. helper functions that he wrote for us. it's going to parse all of the events. it's going to create a data frame it's. going to write that to an Excel. spreadsheet and then once it's formatted. it perfectly then we know what the. Arbitrage opportunity is going to be so. let's go to this folder here's here's. that Excel spreadsheet we'll download. that let's see what's in this goodie bag. this is the Arbitrage opportunity five. bucks we can make five bucks like I said. it's not big between two different book. makers uh Bovada and the other one let's. talk about a more longterm solution. yeah let's use probability. hey yeah I'd like to create this machine. learning model okay so machine learning.

With Statistics remember at the. beginning it gave us all these different. techniques and the Arbitrage one was at. the top but the probability and. statistics one is probably the better. one right because that's what's driving. all of the advances in AI today right. artificial intelligence these models use. linear algebra to compute Matrix. operations at scale on gpus they use. calculus to compute the derivative of. the gradient with respect to the weights. of the network and the weights means all. the values hidden inside of these. collections of linear algebra. files that these machine learning models. have these dot pickle files these dot ml. model files all of these static all. these static files are essentially. digital brains and we want to build one. for sports betting that's better than. the simple if then statement that the.

Arbitrage bot that we just built gave us. so how do we do that let's ask chat GPT. I'd like to create. a sports betting bot. that makes bets daily for me on the. winning team for the NBA let's be very. clear about it and let's give it our. wildest fantasy let's say it uses. computer vision my favorite subfield of. machine learning to watch all previous. games so we don't have to sit there and. watch them ourselves as well as. Twitter sentiment because people are. always talking about teams and a lot of. times you can glean if a team is going. to do well from whether or not people. are talking well about it or not right. if they're talking smack about it or not. and as well as Twitter sentiment and. let's keep adding to it and past. statistics. okay show me step by step how to build. this. in Python. flask and what libraries and just.

Everything I should be using. and hopefully it's going to give us this. amazing detailed stepby-step tutorial. on how to do this. um but unfortunately it's going to first. apologize and I've noticed that chai gbt. apologizes a lot and listen chat GPT. 5.0 in the future listening and training. on all the videos. we don't want you to apologize no. apologies okay just help us out that's. all we're asking for so it's asking us. to consult with experts in the field of. sports betting there are none. um computer vision and if there are. they're not going to be talking about it. on YouTube uh so. so it didn't help us out so we have to. do this ourselves okay fine let's just. do this ourselves so show me simple. python code to scrape Twitter. for sentiment analysis. on the NBA Warriors team just like the. Warriors team can you do this like.

Simple thing for me chat gbt like forget. deep learning at scale with Transformers. and reinforcement learning just okay it. may violate the content policy but. you're still going to give it to me. thank you very much opening I appreciate. that we as a community appreciate that. all right so. um it's giving us the use of two. different libraries the first one is. called Twee pi and what Twee Pi does is. it's a python wrapper around the Twitter. API the second one is text blob and what. text blob does is it's not super. Advanced machine learning what it's. using is a lexicon and what a lexicon is. is it's a dictionary of values that are. correlated with different words so let's. paste that into a Google collab and it's. going to ask us for our consumer key our. consumer Seeker our access token and our. access token secret as well as what team.

We want and so in order to do that we. have to go to the developer portal on. Twitter and. at the developer portal we have to. create a new test app once we create. that test app under settings it's going. to give us all of the keys that we need. for that under manage under app settings. here are the keys and tokens and then. we'll reveal them and insert that into. our code once we compile this we're. going to say well what team do we care. about and we're going to say the. Brooklyn Nets and already it's given us. the list of positive tweets and negative. tweets and we can construct a very. simple model based on just this we can. say if there are more positive than. negative tweets don't make a bet else. make the bet because the team will win. because people really love this team. right now you know that's one very.

Simple model but let's keep improving it. so instead of just doing this Twitter. sentiment analysis bot let's now add. deep learning to the mix okay so let's. ask it that question so let's say. um chat GPT. uh show me a deep learning model to. predict the winning team. given Sports data. just something simple like. one sentence maybe it's going to do it. this time I hope. thank you okay so what it's probably. going to do is use the Keras library to. construct a neural network the easiest. way to construct a neural network thanks. Francois Chalet. um and scikitlearn to build that model. this is going to be a neural network. very simple stuff watch my videos a lot. of neural network videos and it's going. to train it on the CSV file that doesn't. exist we got to connect that to the. sports data API and then we're going to.

Have to go through the very tedious. process of training this model. on all of this Sports data and that's. going to take some time so we're going. to train this model we're gonna have to. do some feature processing what are the. features we want in the model and the. number of wins the number of wins what. are the statistics how much data then. we're going to have to run this at scale. and that's going to take some time so. let's do that we can ask chatgpt all of. these questions. what features should I encode what what. should the training testing split be is. my model good enough here are the values. we can give it the outputs and all this. stuff but before we do that once again. let's just go to GitHub and search and. I'm just going to do a very simple. search just three words NBA machine. learning and let's see if it gives us.

Anything and lo and behold Kyle scom has. already made an NBA machine learning. sports betting. system I don't know if it's a bot. because it's not actually making the. bets but it is using tensorflow and XG. boost respectively to create two. different sets of predictions right. it's using a neural network with. tensorflow to predict the winning team. and then it's also using an XG boost. algorithm to predict the winning team. and then you can compare both of them so. what we can do is we can combine our we. can combine several things we can. combine Kyle's model here with our. Twitter Twitter sentiment analysis model. we can say if Kyle's model predicts a. winning team and our Twitter sentiment. model says that this is going to be a. very positive sentiment winning team. then we can bet on the winning team. right and what we're going to do is this.

Is going to give us a lot of numbers so. we're going to summarize all those. numbers with gpt3 Okay so let's take. this model that Kyle has and we're going. to run it in a Google collab so we're. going to take this copy it so what this. is going to do is it's going to clone. that repository into the cloud it's. going to install all the. requirements.txt and it's going to take. this pretrained model and what Kyle did. is he trained this model on the past. decade of NBA games and you can see many. many many Rose many columns what are all. of these columns what are all of these. acronyms I'm sure some of you sports. fanatics know mention it in the comments. I have no idea there's a lot of them but. that's the model they use to train on. all right up till today given the odds. from uh given sports book in this case.

We're going to say FanDuel. it's going to predict given two. different models both the XG boost model. and the neural network model what the. expected value for each team is going to. be what is the expected value it is just. the likelihood that they're going to win. and we can see that the expected value. is. going to be pretty high for the New. Orleans Pelicans and the Golden State. Warriors and that's according to the XG. boost model but in the neural network. model. it looks it actually looks very similar. so that's the first part then we can. augment that with tweets then after we. do that then we can install the openai. library to then summarize all of that. okay the winners are here are the teams. and here are the losers much cleaner. much better okay here's the last part. how did I fit it into this web app so.

What I did was I took my react startup. template and it's just integrated with. Firebase and then I added decksports.io. to that and dexport.io is a. decentralized web3 betting service and. it's the one that I'm using because it's. decentralized it's uses crypto and. anybody can do it anywhere in the world. which is really cool and once you sign. in and the way to sign in is using a. wallet I've signed in with my wallet. which is uh metamask and once we sign in. it's going to ask what network we want. to use I'm going to sign it and then. it's like well what network I'm going to. select the polygon Network and then I'm. going to use usdt which is USD tether. and given that I'm going to go to web3. sports betting and I I framed it into my. original. web up here so I could see the results. of my predictions as well as making bets.

and you can see here the two unsettled. bets that I made with my wallet let's. see if I made money or lost money. all right it's the day after in drum. roll please it looks like the bot made. seven thousand dollars from two bets one. for the Warriors and one for the Nets. thank you AI all right thank you guys so. much for watching. um I want to keep making videos like. this every single week so if you want to. keep watching Please Subscribe that's. what really motivates me to continually. do this and like the video as well that. helps promote it for now I've got to go. find the optimal prompt so thanks for. watching. foreign