chris2411

@melossinglet

I have to say looking at the video, it comes across as too 'wow look at me', next time i'll add the detail as the majority of people have asked for the process. It's really not good enough to slap down 4 predictions and say trust me.

I really wasn't expecting the genuine interest from some very knowledgeable people in the sports betting field. Getting their opinion (which was the missing link) with some computer science and probability, really looking foward to the future of this.

I still stand by my model. Based on stats even going years back Chiefs have never played like that. So what was the model supposed to go on? complete feeling, love for Bucs?. Notwithstanding that's the reason why I gave 4 predictions, and 2/4 is a result for me. My friend from Europe sent me screenshots just after the game, she made a 28% profit on my picks.

I am slighty dissapointed, however I can also say I really feel I have something good going here, and will continue to help others in the thread with their models for the upcoming NBA and next seasons NFL, thanks for all the support

 https://i.imgur.com/vy6qslZ.png

https://i.imgur.com/xiM1hwq.png

@brownidentity

I would be glad to assist you in any way I can. Google are doing some exciting things, however considering their resources are being outshone but many small startups. My nephew loves stats and NFL so I was fortunate to have access to his extensive library. I also use aws and next-gen stats, perhaps the best sports stats resevoir ever created. Auto-scalp is used for injury updates and news from ESPN.

Cheers, Chris 

@VegasCompetitor

 Thats fair enough I didn't expect everyone to get it. I have to say the experience and ability of people who have approached me on here has been phenomenal. It takes a different breed to be a gambler in a zero-sum game.

Question: how is Vegas now considering nealry all states will have legalised sports betting by 2025-30?, Does Vegas really have a purpose anymore, other than elopements? 

@wizard1183

I've spent a great deal of time thinking about the human element in this project, and trying to understand what it actually means. What makes my model stand out is that it accounts (primitively) for the psychology of the players particularly the QB. In a Super Bowl Brady goes quiet from all stats available, he relies on familiar patterns built throughout. Also the reluctance of Mahomes to run in the last 3 games is blinding obvious on paper. I know that when I see a prediction I don't really know how likely it will be to translate in real life, that is why some quality control from a person who understands the human element to teach the model is much needed. 

I'll compile this seasons results, should be up this evening (from sql to excel).

Cheers for taking an interest, Chris

@vanzack

I cannot say this is analysed in an extensive way, as there would be too many variable with little data available from player to player. The main parameters for players are their rankings at their position. So average running yards, passing yards, sacks, funmbles (essiantially my own modified power rankings) however because this is so superficial it is clearly a weakness. Personally I feel if someone/a seperate model was able to know the players very well it would definitely be an edge, I simply do not know enough about the players and don't want to get too invested as emotions would clearly cloud my judgement. 

At the moment I simply don't have the knowledge or computing power for such an analysis (or mathemthical ability tbh) to analyse each player. But if there was a way of condensing or averaging each players contribution I am certainly willing to try.

What is the most important factor when analysing how impactful a player will be as an individual?

Cheers, Chris

@barneybeans

Really great post. As you can probably tell I'm not a big NFL fan. I don't follow drafts or stories surrounding players (sort of the reason why i'm posing here as I know I need a new angle). I like the math behind games. Football in general is a very simple game. The permutations are small compared to soccer or rugby. The offence either completes or doesn't no second chances per drive. My feeling is that people tend to individualise games making a god out of each and every QB. Really it boils down to completion % and yards gained.

If you compare that with Rugby (A sport which I know quite a lot about) it's just so much harder to predict. Fatigue sets in and anything can happen whereas in football all players are ready to go, full concentration. It means there are very few plays that stray too far from normal. Like how in the NFL there are nearly always an average of 10 points scored in the 1st quarter. When compared to soccer the first 15 minutes average each year bounces around with no discernible patttern. 

But yes I agree Data is king, and lack of meaningful data is a real flaw in this system, and why people steer clear of it. I however look at it like, If I have the same data as my competitors and analyse better, the model will always be more accurate.

Cheers, Chris

Just to update anyone on the thread the line had moved to the chiefs by 12. The unders looks the way to go and Brady throwing yards over and mahomes unders is the same,

Cheers, Chris.

@thorpe

I agree with your first point. However I am not selling a course or picks and never will, but I realise how it looks when taken at face-value. The subscribing comment does sound a little foolish, but I really just wanted people interested in predicitve analytics to reach out and engage and maybe form a community, didn't really know how else to word it.

Regarding the second point I clearly remember my high school math professor talking about the dangers of continuous rounding, as well as in college. Ever since I try to leave as many decimals in the programme as you can get results very different with each operation even with 4 sig figs. My general rule is to leave them as it doesn't effect the outcome in any real way. Any predictions will be far from current bookmaker line to be counted as a prediction so working with such small numbers is insignifcant relative to the whole number parameters. That was a screenshot of the programme interface so rounding wasn't possible. 

I will train a duplicate model using signifcant figures throughout just for you (and to prove a point) and see any differenceis found in accuracy. 

From my conversations, it does seem that getting rich in sports betting is more about controlling emotions and bankroll managment, however time will tell

Cheers, Chris.

@vanzack

I work with European odds (much simpler to put into a computer) always over 1.83, with an avarage bet of 1.89. I'll assume you are familiar but for anyone reading that means for every 1 dollar put down you get 89 cent. 

Quite cheesy but people like you are the reason I wanted to branch out. I know enough financial analysts but it takes a different breed to try to predict sports. Bill Benter has been a hero of mine since reading his book about 12 years ago. 

I agree, the most profitable thing I did with my model is not to try and force a prediction for every game. If the model is not sure it will not give a prediction and look for another market in the same game.

Yes, I will not sustain the prediction accuracy (just by the law of avarages) but I know I can consistantly 60%. Again quite cheesy but money doesn't motivate me, it's more pushing the boundaries of AI and getting more people interested so this field can expand.

Cheers, Chris

@vanzack

 Yeah I didn't want to go into details initally as most people just want the sexy headline. Being mathematically minded, I also would never take that strike rate at face value (you'll be surprised how many do, especially in finance). I have a SQL dataset with the outcomes and bets that would have been made from them based on the line Bet365 are offering. ALL these bets are against the spread and NONE are moneyline (that would be too easy). I personally don't bet so I didn't wager any money per se (I will be having a cheeky one on the superbowl, just to stand by my predictions). There were approximately 3 wagers per game and most games got a bet for all of this season. 624 bets (154 on the spread, 240 on passing yards, 134 under/overs and the rest miscellaneous). These were always a single bet (NEVER parlays etc.)

Do I expect a similar result again? no, it's unheard of. However with the right money management and no emotions It could prove very profitable.

I am making my next YouTube video on exactly how I train my models, how I get data and the parameters used. I hope this answer your questions, thank you for taking an interest. I would love to know if you are training any models currently, and how successful are they. Whihc parameters do you find to be most successful?, Cheers 

@brownidentity

I have to say i've only ever used GPT-3 for voice recognition and similar tasks and wouldn't know how it would work for linear regression for predicting outcomes data in sports. This space changes very quickly in finance not so much in sports analytics so it's fairly easy to make a good model with primitive software. I find the limiting variable is easily Data, that being said next-gen stats aws and others have made football and basketball to a certain extent the best sports in the world to analyse. 

I'd just like to say thank you guys for the kind words and support. I hope to do many more videos, perhaps more slowly 

@DeLoreanStylez

 I've been on the chatrooms too long to not know if that's sarcasm, so i'll just assume you are serious.

If you are thank you for such a refreshing response. I worked for serveral years in investment banking basically as a quant working with securities, and programing models for high freq trading. I consider myself to be more inteserested in the mathmatics behind this. I don't really bet but if I did it would be on football. The play by play nature makes it very easy to analyse and and edge is gained fairly easily.

My machine learning model Automatically scalps any sports news sites for weather, injuries etc. and decides whether it will effect an of the other variables. It is a supervised model meaning I had to programme it first however now it corrects itself without me, and gets better with each game.

This was an amazing season 87% strike rate on all markets (taking into account variance). I am very keen to see where I can take this perhaps build a group for predicitve sports analytics. 

Thanks again for taking an interest

Either I can assume people are apathetic towards the subject or just downright annoyed, eitherway lesson learned

These are my model's 4 predictions for SuperBowl LV.. I know sports betting in the US is legal for the first time for many. Although I do not bet and this is a purely academic project I hope this provides you with extra data. Coming from a Quant background in finance, creating models for securities, I have to say American football lends its nicely to analysis unlike other sports. The play by play nature make patterns very obvious edges quite easy to take advantage of.

I have predictions from my machine learning model. So far this season it has a 87% strike rate on all games and markets.

Looing to know what everyone thinks about them!

Cheers, Chris

@ActionMagnet

I have predictions from my machine learning model. So far this season it has a 87% strike rate on all games and markets ( even when adjusted for variance).

https://www.youtube.com/watch?v=FT-BvjtinLs&ab_channel=ChrisMcGowan

I have to admit coming from a Rugby background I probably lack the insight into the dynamics. However I always remember watching at least the SuperBowl since I was a kid. That being said I know a lot about data science and investment banking quantatative analysis so far it has translated nicely into sports. I personally don't bet but would love to see the realworld application of predictions which invariably has to be sports betting

Looking foward to knowing what you think about them!

Cheers, Chris