Hi, can anyone recommend a good source that has reliable creatine that is cheap? I am buying in bulk.
I am not a gymmer, I am buying it for the cognitive/brain benefits for work lol.
But I want to do 5 to 10g daily so I need a bulk source.
Like, my main symptoms are crushing fatigue/brainfog and some muscle weakness (I can still walk long distances but slowly). But at night when the fatigue lifts I feel almost normal sometimes.
Hello, does anyone know how to get this data? For big 5 European football leagues.
Like eg so and so ran 12km this match, etc.
Nmecha just played a ridiculous vertical pass to Undav for Germany’s winner….
I’m doing systematic football betting
I wonder if verticality of passing can be quantified as use as a metric for scoring midfielders
Hello, it’s me again. Just wondering if anyone uses play by play datasets for football soccer fundamental modelling. Aka moneyline
It’s the only dataset category that I cannot get because as a retail I cannot go and pay Opta or Statsbomb a fuckload of cash.
If anyone does use it, would appreciate to share what they use it for (of course you can leave out the secret sauce details).
I have lots of data which I scraped from various sources, built data pipelines and scrapers and validation, over the past 2 months of building, from various websites - Transfermarkt, Sofascore, Fbref, L’equipe, BBC, Sky, football-betting, markstats, sportsmonks etc.
I am aiming to do moneyline betting for next season for big 5 leagues.
I am looking for people who might be interested. I am doing the research myself, having painstakingly scraped data, but it would be fun to do research with someone else and test hypotheses and bounce ideas. I have a big list of ideas I want to test through in systematic fashion. It is also abit lonely to not have anyone to bounce ideas off.
Requirements: Decent Python skills (enough to understand what Claude puts out) and interest in football betting. Decent statistics understanding (aka common sense)
Please shoot me a DM if interested. Thanks. I am willing to share my datasets so you can do your own research on them too.
I can only talk through my ideas and research so many times with myself before I go insane.
I am just curious as I am mild and cautious about my baseline. I would like to try and learn things to avoid.
Would be good to hear some of your stories and if you managed to regain the baseline.
I am scraping some sites (sports journals) and it is an absolute bitch to scrape.
Reason is because I believe these local journals know their domestic league better than the analytics providers.
So is there a correlation? Obviously, the harder to scrape, the less people have the data. But it could be a bitch to scrape then end up being useless too.
I am building a football analytics engine and need player ratings. I have Whoscored ratings but I do not trust them as they are algo generated. I am looking for a journal that publishes match player ratings for La Liga.
I am looking at Eldesmarque.
At least in football, a new manager and new transfers should add a “drift” term to team performance over the course of a season.
My problem is, we know certain players and managers outperform or underperform their expectations. This results in an over or under drift term attached to win rates across a season.
An example in football: Florian Wirtz from Bayer Leverkusen and Isak from Newcastle to Liverpool in 2025/26.
The expectation was that Wirtz would take Liverpool to the next level and help them retain their title. He cost 120m and was destroying the BuLi. Then, he flopped horribly at Liverpool and was a reason for their poor performance in the 2nd half of 25/26. The reason is because Leverkusen centered their system around him as an attacking pivot, and the BuLi is much less physical than the PL. Wirtz simply couldn’t cut it, he was often outmuscled in games and seen as weak.
Another example: Carrick took over United in 25/26 Jan under a struggling Amorim and got United winning until they went from mid table to top 4 in the second half of the season.
He would score very high on the drift term for a manager, but the problem is, prior to United he had almost no track record.
Can we quantify this over or under performance with past data? Or for players, is it already “priced in” into the player ratings? For example, we see Wirtz had really low Whoscored ratings
So, how do you price this in? Transfer budgets or market values are ex ante. Wirtz would score really highly if we used “transfer fee or market value” as a proxy for squad strength, which we apply to the “cold start problem” to games at the start of the season.
A problem I’ve been thinking about in my modelling process.
One approach might be to assign a transfer score based on discretionary research, aka watching games and the eye test. For example, I knew within the first game of watching Wirtz play, that this guy was never gonna make it. I had that gut feel. And it turned out to be right.
If so, how do we integrate a discretionary factor into a systematic process?
I know in NBA they have all kinds of player metrics like DARKO, LEBRON. How predictive are these of future performance? In football, these don’t really exist.
Does this apply to other sports? In football, there is a phenomena as I said where certain transfers just really flop or outperform the consensus.
Football is full of stories of expensive transfers flopping and cheap transfers doing way better than everyone thought.
This is a very interesting problem that perhaps cannot be quantified or modelled. But if there was a way to capture this idea, money can be made.
At least in football, a new manager and new transfers should add a “drift” term to team performance over the course of a season.
My problem is, we know certain players and managers outperform or underperform their expectations. This results in an over or under drift term attached to win rates across a season.
An example in football: Florian Wirtz from Bayer Leverkusen and Isak from Newcastle to Liverpool in 2025/26.
The expectation was that Wirtz would take Liverpool to the next level and help them retain their title. He cost 120m and was destroying the BuLi. Then, he flopped horribly at Liverpool and was a reason for their poor performance in the 2nd half of 25/26. The reason is because Leverkusen centered their system around him as an attacking pivot, and the BuLi is much less physical than the PL. Wirtz simply couldn’t cut it, he was often outmuscled in games and seen as weak.
Another example: Carrick took over United in 25/26 Jan under a struggling Amorim and got United winning until they went from mid table to top 4 in the second half of the season.
He would score very high on the drift term for a manager, but the problem is, prior to United he had almost no track record.
Can we quantify this over or under performance with past data? Or for players, is it already “priced in” into the player ratings? For example, we see Wirtz had really low Whoscored ratings
So, how do you price this in? Transfer budgets or market values are ex ante. Wirtz would score really highly if we used “transfer fee or market value” as a proxy for squad strength, which we apply to the “cold start problem” to games at the start of the season.
A problem I’ve been thinking about in my modelling process.
One approach might be to assign a transfer score based on discretionary research, aka watching games and the eye test. For example, I knew within the first game of watching Wirtz play, that this guy was never gonna make it. I had that gut feel. And it turned out to be right.
If so, how do we integrate a discretionary factor into a systematic process?
I know in NBA they have all kinds of player metrics like DARKO, LEBRON. How predictive are these of future performance? In football, these don’t really exist.
Does this apply to other sports? In football, there is a phenomena as I said where certain transfers just really flop or outperform the consensus.
Football is full of stories of expensive transfers flopping and cheap transfers doing way better than everyone thought.
This is a very interesting problem that perhaps cannot be quantified or modelled. But if there was a way to capture this idea, money can be made.
Hi there, was wondering if there are any people who managed to bet on Polymarket from Singapore.
It’s technically banned but I heard on the grapevine many people are doing it…. If you have please DM me! I’m trying to set it up but am not succeeding, likely due to geo restrictions etc.
Yes I am using NordVPN and I have topped up USDC, my problem is getting blocked when placing a transaction.
I am building a systematic football betting engine , pipelines and research ideas/hypothesis but the irony is…. Betting is very much banned in my country.
Could you kindly DM me. I'm having some trouble placing a trade. I managed to send a paltry amount of USDC from coinbase to my hot wallet, then from my hot wallet into polymarket. However, when I click to place a trade to test, it says
"Your account needs to migrate to a new wallet to continue trading. Deploy your new deposit wallet, then transfer your funds over."
And when I click migrate, it says "wallet is still deploying. please wait a moment for it to deploy on chain". It's been 30 minutes.
I am on NordVPN.
Did anyone run into this issue or anyone from Singapore managed to place a trade successfully? Please share here or DM me! Really need some help here...
Hi, so I’m an aspiring bettor. I am a commodity quant by trade and I am doing this as a side hobby. Obviously my dream is to be successful in this enough that I can do it full time. I have no idea if it will work or not.
So what I have are ideas. This is from watching football and wondering if we can quantify certain football wisdom or tropes. From my ideas, I try and ask what data I need then I scrape.
I’m in the process of building my pipelines. Then I will do research to test my ideas.
However I see many people here that don’t watch the sport they bet on. How do you come up with ideas for features? Do you just explore the data first and see what correlations you can find?
Does anyone know where to get this? Or is it only constructable from shot by shot events data?
For example, a time series showing the possession scores every 5 mins.
If in the 70th a team is 2-0 up I would see the possession score drop as they sit back. At least that’s what someone like Arteta would do
As above. For my football betting engine. happy to collab or dm.