# Football betting tips - Predicting correct score odds

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Knowledge Has expanded, significantly I've. Got access to more data and stats and I understand there's little bumps and nuances, quite, well the. Problem is I've got to try and get it into one video so I'm going to give you a, simplistic. Model here that will allow you to at least get to that first step but, also it will give you the hints and tips that you need to, understand what you should be doing why you should be doing it and how to sort of get on to the next level so, I'll throw in a few of those things as we move through the, video, so. Yeah you know what is behind me what are we looking at here in the, background. Well. What we're looking at here is a database, of seven, thousand, three hundred and eighty four matches. Now I have an absolutely enormous, database. Of. Matches, across, different leagues different, countries different competitions. Different. Stages of those competitions each one, of them sort of tailored, to, be more specific to, certain scenarios. Whereas, this is a generic database, this. Is actually the English, Football. League and. The English, Premier League all. I can't, remember the exact details of what it is but there are best part of seven thousand odd matches, within here but. That's what, it's modeling that's what it's looking at yeah within this data set across. The top here you can see how many goes the away team is scored within a match and, on the left on the y-axis you, can see how, many teams the home score at the home team, has scored within. That particular match so, we can see if we go nil, nil you can see there were 640. Matches that ended nil nil in our, sample set of seven. Thousand, three hundred and eighty four matches, and you. Can see a variety different scores here so the number of matches that ended up 4-2 was. 69, and the number of matches, the number for three were thirty four in that samples that you can see it's quite a small percentage, of all, of those matches and, you can see most of the results were clustered, over here at, sort, of nil. No. Goals 1, goal or two golds that's sort of where most of the four matches are clustered. So. Yeah. We've got the core numbers here, if. You're. Using, this seriously then you would probably choose, data set specific, to your need rather than a generic one but we're going to use a generic one today to, get, across the concept for you. Instantly. There is a lot more depth here as well so, you. Know it's possible for me to go to excruciating. Ly detailed. Level but that would just take far too long it, may be something I do in the academy at some point but yeah I digress, so. Here you can see I've converted the, chance, of a correct score into, a percentage. So we can see here at, the most common correct, score within a market is 1 or home. Team tends to win more often than, the away team but, that could be one nil turning or to one. But. Overall if you're looking at forecasting, a correct score if you say one, or you'll. Get it right more frequently than you get it wrong in. Terms of picking, a correct score is what I'm talking about so. You can see here that, the. Distribution, of scores and you can see a 1 nil 1, all is the most frequent one nil is the second. Most likely, score 2, 1 is the third most likely score 2, nil and. Nil, nil comes. In around that level as well and then it's 1 nil to the awaiting so you can see there's, quite a tight cluster of, matches. At, low scores that generally, occur so if I move. My mouse across. What. You can do is you can add up all of those individual. Results, so we've got here and it's, on the bottom of the screen here but. For. The for the purposes of this video I'll do, it sort of out loud for you so you can understand, what I'm looking at so ten percent plus twelve percent is twenty two percent plus we've got sort of another. Nine percent here thirty one so can you see if you add up all of these figures that gives you so, if you were dutching, for example, you. Can have a look at these stats and it, will identify sort. Of clusters, of results, that are likely so it gives you a hint as to where you. Can actually add up all those things together but yeah you know roughly, speaking ten twenty thirty forty fifty. Sort. Of 256, ish, or there, abouts early 50s it covers all, of these scores were, the home team wins all the way team scores one got, so. Yeah you can play around with all of these numbers and that gives you some sort of general feel for the way that a football match is likely, to, play out so. When you look at Memphis stats like this you realize football is quite boring most of the time and there's. Not a great deal of interesting, stuff going on in a football match a, lot of the time the scores are quite low typically. So. How do we use this to actually predict a correct, score because I've sort of said here well you know one. All is the most common score but, of course some matches will have a strong home team some will have a strong away team, and.

That, Will influence the outcome of it as well but typically where you would start is by predicting, the draw because the draw is. Something that's relatively easy to sort of understand, so, we've. Taken this, data that we've got here we've stripped out all of the Home & Away wins and therefore. We are left with, a draw, and you can. See that what I've done is I've taken away all of the numbers around everything, other than, the draw so, twelve, percent of matches, ended up 1 or 8, percent nil-nil 2%. 2%. 5% were, roughly to all and you, can see all of the data from here and you can see it really thins out when, we get beyond 5 all I have, seen a 5 all match, but, in this particular data set there were none and there, was a 6 all in a Scottish Li he could try and remember than what the match was can't remember off the top of my head so. They do occur, that just very very infrequent. So. If. We look at this we're basically saying that there are five ways, that. Matches it typically drawn and most, of those are going to be nil. Nil or 1, nil, in the, scheme of things and there are a few tools and, there are some thrills which are quite rare but. Beyond that it, gets pretty, thin so you, can see these numbers up here have, actually replicated, down here I've just taken these numbers and dropped them down to, this individual, line so, you can see how. That translates into what we're about to do next so here you can see draw frequency. And. That's what that, they have obviously abbreviated, it there so, what, is that talking, about well we've. Added up all of these draw figures here and that equals, 27.06, percent, so we're saying that 27.06. Percent, of matches, end up. In a draw. So. What we're trying to work out is the percentage chance that if a match ends as ends up as a draw that, it will be a certain, type of draw so. What we're doing here in fact what I can do is use Excel. To show. This for. You they, go couple of arrows we're, basically taking this value and dividing it into that value and the reason that we're doing that is we want to know. How. Many you know what chance is there of a draw occurring, we know that's 27% but watch ants of a draw recurring and it being nil.

Nil So. If we divide that by that 32, percent of draws end, up nil nil, 45%, end up 1 or 18%. To, war and then you can see it drops away from, that particular point that moves us on to the next step, so. In, reality, the chance of a 1 all draw across, this entire data, set should. Have produced odds of eight point one nine eight, point two now. In decimal odds so all I've done there is I've just converted, the chance of something occurring. Into. Its. Specific. Set of odds so. Because. I typically use an exchange we use decimal, odds we don't use fractional, so, I've just done 1 divided, by the chance and, that's where that number comes from but basically we're converting the percentage, chance of something happening into. Decimal odds that we can use to understand, if there's value being created, on the exchange or not, now. Of course you, know each. Individual, match is different, so the. Chance of drawing one match of the home team winning or losing is going to vary quite dramatically, from one match to the next so how do we take account of that well, you can see what I've done on here is I have a thing, that I've called market, odds so. I've gone into a match just before, I set. Up to. Record this video it was West Ham V, Everton, so, I'm looking at the West Ham V Everton match just above the camera here and I, can see that the draw, odds are 355. So that represents a 28%, chance of, that, match ending, in a draw so, if we believe that the market is efficient, which it generally is and. Certainly on an exchange one of the reasons that we use exchange, pricing, is. Because, it's much more efficient the the overall book percentage, on the. Exchange here is 100 point one so, it's basically saying that that's near-perfect there's no margin being lost to the other side of the book not. Going to explain the specifics, about that but, basically the market is very very efficient, when, we look at the market in this way, and. Therefore we're saying if the market is all-knowing and very, efficient, and. We, assume that it's priced this correctly, because I'm pricing, it other people are pricing it we're all trying to get the perfect price. Then. The draw has a 28 percent chance of occurring, within this, particular match. So. What we've done here is we're. Saying well the chance would draw slightly higher and then. The database set, that we used. So. How would that translate into. The. Correct score within this particular match, so. If, we look at. We're. Looking at this data up here we're, looking at the chance of a draw being a certain, type of draw looking. At the chance of a draw from the date set and then we're comparing it to this, particular match these. Are the numbers that it pumps out so. Again we'll have a look and see what it's doing here if. We look at. I'm. Just writing hasn't really Illustrated, it particularly well has it. But. Basically what we're doing is we're taking the, chance. Of it being a certain type of draw we're, taking, the, odds, that.

And. Like I say there are other levels, that sit behind this, but. The. Purpose of this video is really to give you an idea of how you, would start to approach this problem there, are many variations within here for example we. Have yet to talk about the number of goals that we would expect within, this particular match, and comparing, those but. That's another video that would last about half an hour just on its own. But. As a consequence, you can see that we're beginning to form the basis of an opinion within the market, and we can do this all just by looking at the match odds we don't have to look at historical data, historical. Results trends. Winning. Runs and streaks and all of that sort of stuff what we're doing is we're looking at the match odds market, overlaying. That on a much much bigger database, and saying well how does this match compare, and adjusting. For the chance of the home team winning or. The the chance of the draw how, does that compare and what sort of results would we expect to see in the long long, term when. You see those discrepancies appear, it's then that you have to decide why those discrepancies there and what has. Caused those discrepancies but. Also probably you would want to refine this model as well if you're going to use it to really use any serious money because. What you're attempting to do here is say, I'm, right in the markets wrong whereas, typically you attend to assume that the market is right and that you're wrong but, nonetheless. This, is a step along that path to allow you to start looking. At the market understanding, the way that it's prior and. Making a judgment on that particular point. So, you know whether you think that there's value there or not or whether the markets wildly, out based, upon a range of different assumptions. So. You, know one of the things that I do is. I go back and look at specific, matches. So I've got a database of all the matches and all the odds that were available and, then I start overlaying those as well and then, comparing, what came out of those results just to see if that, sort of fits so.

There's An element of that fitting, going on there well what we're essentially trying to do is look at a market make, a judgment on what we think the price should be and. Then make some assumptions, and judgments from. That particular point and of, course we can do this on their waiting now but, one of the things that you'll find within football is all the markets are interlinked, they all, look. At one particular aspect of the market or the other there, are some core values that sit behind that again that would be an entire video in itself, but. Nonetheless those, core values do drive all of the pricing that you see in the market whether it's the both teams to score over and unders correct, score match odds and any. Variation. Of all those are all linked into these, data and you can transpose. The data, overlay. It on existing, data sets and start to contrast and compare to see if you can find some value or an opportunity within the market to do any type of betting, or trading, strategy. Anyhow. Yeah. There's, a simplistic, overview of how to predict, correct. Score odds we. Use an existing database put, in specific, data around this particular match and then start looking at the market and a little bit greater depth from there so I hope you've enjoyed video I hope that was useful if. You got some comments please leave them below and. If you liked this video and you thought it was helpful then give me a big thumbs up because in. My. Database and in, my mind there's, a million different things that I could talk about but, I rely upon you to tell me the stuff that you find interesting so yeah I hope you found that interesting and hope that aids you whether, you're betting or trading on football. You.

2019-04-02 02:03

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Great video. Explained a lot.

You spoke all day and said nothing

LOL, I just explained how to derive correct scores from any match odds market. Something I've done for years to price football markets. Yeah, nothing was said.

Hi Peter I would like to know where I could get the information of this deeper level of the precification model you made. What the next level would be and where I can get this information there is any book that you recommend ? Cheers

It gets very academic from this point on. There have been quite a few papers on the subject but you will need a decent level of maths to get to that level.

i just say it. i like you video but i thing it would be better if you kan make a papir/word dokument or somethink where you show what you betting for this bpl round or other leagues

Fergies last League game ended 5-5 away at WBA if memory serves me correctly

That's right, I remember that now. I also watched Southampton draw 5-5 with Coventry a few years back.

I think you're confusing your data with the old Two Ronnies sketch. Was the 5-5 draw between East Fife and Forfar by any chance. :)

WHAT ABOUT WIN OR DRAW?

I have actually done the win and draw on this video, an away win would simply be a NOT home win or draw.

betangeltv, thanks for your answer my background is in physics but now I’m migrating to the Data Science world, I have a few spreadsheets using poison to predict results, but would like to know a different approaches so any references would be really appreciated. Thanks again for the reply and for sharing your knowledge with the community.

The purpose here was really to explain the concept in depth rather than apply it to a specific set of fixtures. You would simply repeat this process for each fixture on your list.

The data is freely available on the forum, see the link in the description.

Somebody pointed out that Alex Fergusons last match in charge was a 5-5. I once saw Saints played out a 5-5 with Coventry. Crazy match.

hello Peter, congratulations for the video, can not find excel? can you put a direct link? thank you

+betangeltv please explain further?

I am so happy that i found you, not many people are sharing such great info and indepth analysis on these things! May I ask which database you use for getting info?

The stats can't lie. Football is the most popular boring game where virtually nothing happens. Football wins the Boredom Stakes with 1-1 romping home two boredom lengths ahead of test match cricket where you can play for five days solid and still end up in a draw. Or worse still, buy a ticket for day five and stare at an empty field because of a batting collapse that ended on day four. I hate sport. Playing arithmetic is more fun. As the old advert had it ''It matters more when there's money on it''

Do a video where all people can understand please. Maybe a 2-minute presentation. A simpler version, what to avoid when betting.

I'd love to do something simple and effective, but in reality there are no shortcuts.

If you visit our forum. We regularly share very high quality data in there that you can use for your own research, check it out!

I think I put a link in the description on the video? If not let me know if you can't find it and I'll put you to the place in the forum where we have this data.

Thanks, Peter, I can not find it.

+4 traders I just checked and the link is in the description of this video.

+betangeltv Coventry were involved in another high scoring game 5-4 against sunderland last week there funny enough

+boTrader I just checked and the link is in the description of this video.

Money talks, BS walks

+betangeltv which forum are you referring to?

@betangeltv which forum are you referring to?

@boTrader I just checked and the link is in the description of this video.

@betangeltv please explain further?

@betangeltv Coventry were involved in another high scoring game 5-4 against sunderland last week there funny enough

@Shokotoko I just checked and the link is in the description of this video.

Nice

Hi...thx for this video...but what if the difference between spreadsheet and market are too high? For example if the hosts win the chances on spreadsheet are 65% and the market gives 28%? How shall we judge that?

Anyone here?

It would be odd to see an error of that magnitude. But if you did they you would need to understand why the rating is so different. In my experiance of using this method it's only adversely low or high scoring games that shift the underlying frequency.

make this model pick 3-4 correct score what an arb would it be

i am from ethiopia , you are a complete genius i hope i can improve watchin u.