Football betting tips - Predicting correct score odds

Football betting tips - Predicting correct score odds

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So, I've done some popular, videos which. People have looked, at liked, but, asked for more information, one of them was called win on betting which was about valley betting, and. That. Was the process of, finding. A price that's out of kilter with the market and then betting that to, a profit, but, of course people were saying well how can you identify, that something is mispriced. And I've. Also done football, videos. Preliminary. Trading videos where I'm looking at certain things and. The occurrence of those events, and I thought wouldn't it be cool to just merge those two videos together and give you something. That allows. You to at, least make a stab or some sort of, attempted. Figuring, out what the best price is in the market and. What that price should be and whether, it represents value, so. In this video I'm going to talk to you about pricing. The correct score market, in football, if, you're. Interested, in learning to trade on Betfair then visit the BET Angel Academy where you have detailed, structured, Betfair, trading, courses or, why not visit our website where you can download a free trial of BET angel professional, but, also visit the forum where, you can get detailed images, examples, and downloadable. Files and don't. Forget to subscribe to our You Tube channel and click on the bell icon if you want notification of new videos as they're, released, so. I have. A strong heritage in football football is actually one of the sports that I understand, better. Than pretty much anything else and. That is simply because that's where I started, my entire, betting. And trading journey many, years ago if we, whined back. Best part of 35. Years, or so now that. Was where all. Of this everything. That has happened since sort of has come out of I used, to sit at home with my home computer tip, tapping away, entering. In data from a Rothman's yearbook, and that. Would allow me to. Start. Gathering information on football matches and I sort of just did it out, of interest really I, used to watch a little football used to play a lot of football and. I just sort of started. Entering the data and trying to figure out you know how things happen from there part, of the inspiration was my daddy's to fill out the football balls coupon, but. He used to do it randomly and I thought well there has to be a better way so. I started gathering the data sticking it into a database, that I've created and trying. To work out how, those odds were formed in a football match and if I could do, a better job than the bookmakers, and that. Led to me winning a first dividend, on Littlewoods pools so. What you're about to see, in this video is, a. Simple. Summary, of a model that I created some. Time ago now what, I'm going to do in this video is I'm not going to say to you this is like absolutely definitely. The way because, there are little things that you need to know about you. Know the positives and negatives in terms of attempting. To predict correct scores this way so I'm going to insert that into the mix as, we go through the video so you can fully understand, and obviously, in the, time since I first started doing this to now my.

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.

The Draw was likely to occur from the match odds market hit within here and then, we're transposing, the two were merging the two together to produce the new rating so, this is basically saying to us this this you'll see this better when we look at the home-wind market in a second so. This is basically saying that from the database, the. Set of stats. That we had the. Odds should have been about eight representing. A 12% chance we're. Saying here that it's nearer to a 13%, chance of a draw in this particular match and therefore, that the, draw the one all draw should be coming in at about seven, point eight six just, under eight basically. Chance, of a nil nil is eleven. Nine. Percent chance or 11 in decimal odds. Chance. Of a two all is about five percent which would be 20 in decimal. Odds so I'm gonna go and have a quick look I haven't looked it yet so this, is gonna surprise me. Hopefully. In a positive way if I look at the set of odds so. We can see here in fact the draw is priced at. Seven point six to seven point eight so. We're almost spot-on there the, nil nil is 11. But. On the actual. Market it's 14. So. They're basically saying the chance of a nil nil is slightly, less than we have predicted, and, if, we look at a 2 all what, is a 2 all at all to all is priced around 15 and we're saying 20 so. We're saying that. That's. Less likely so. They're saying that the chance of a nil nil is less likely, the chance of. -. All it's a little bit more likely than we're saying so basically, what we'll you know this. Is where some of your skill and judgment as a trader as a value better and your. Model comes, into play because this is and there, are much deeper, layers to this as well so don't forget that see I'm giving you a top level here I'm not saying to you that this is absolutely the way that you should do it because there. Are many evolutions, that you can take place from here in terms, of the way the model market but this is going to give you an idea of the way that, the market is priced and how it's all interlinked, and how you can start to derive stuff from there so, we're saying that we think the nil nil should be eleven point one and nine percent chance but. The market is saying it's 14. So. It's saying that it's actually got less chance so. This is basically saying. In reality that. Probably. There. Were going to be slightly more goals in this match this is what it's effectively, saying because the more goals you get in a match the harder it is for them to be, a draw so, if you've only got two goals in a match you. Know they could be shared equally but. If you've got three goals they can't be shared equally but, also maybe the home team's a little bit stronger or, maybe there's a propensity for, more, goals in this match than average, so, that's where some of your skill and judgment comes into these, sort of models is to understand, where the discrepancy, isn't why you think that discrepancy, exists, but, also the core data set. That you're using should, be relevant to the match and there. Are other layers as well which I'm not going to go into now because I could talk for days about, specifics. I just. Want to give you a broad level to. Look at so yeah the one all is about. Right we're. A little bit short on. The. Nil. Nil and we're a little bit long in the tooth on the tutu so you can make a judgment as to what do you think that's value or not given this particular match. But. What you would do is you'd step through each one of these stages so the next stage, would be basically to look at the home team, so. The home team in this case is West Ham I. Need to go back to the match odds and have a look at the match odds again and see where we are at 262. Yeah that's correct in there and. If. We, look. At, the. Market, itself. Then. We can go through the same process we. Can basically say, yeah. Exclude, the draw exclude, all of the results, that end up with the away team winning and just focus, in on the, correct scores that would have the home team winning and all of those value up they. Come to, 46. Points to 8% as that current yeah. I'm, just looking to see him make just making sure we ain't got any errors here, so. On our database basically. That's saying that the the chance, of the home team winning any, of these particular schools is 46%, however. When we actually look at this match, we've. Entered in market, odds here to 62 because that's what, the exchange is telling us that the chance of on that shots. Market. The, chance of Westham winning is 38%, in the match odds.

So. We've entered that you can see that that's slightly lower than, the average that we've seen on the database so even that tells you something that's, saying that West, Ham playing Everton, they've. Got a slightly lower chance than you would expect on average of a home team to win against in the waiting is that correct do you think that that's valid given. Their league position, given the way that they're playing all, of those things is that a valid assumption to, be made in this particular case because according to your database the. Average home team wins forty six percent of the time and yet, the market is pricing West Ham a fair, bit below, that sort of eight percent below that particular value so, is that valid on this particular match because you could adjust your assumptions, on that basis, now. I've been following I was gonna say I've been following West Ham the season and not, in that sense but I've been following the, results from West's home because West Ham have been throwing up some truth truly bizarre results, this season very. Difficult, to predict so, maybe you pass on this match and try another one but West Ham seem very very erratic. This season. They're. Playing at home against a weaker team and they conspire, to mess it up and then they go away and play. A decent team and they play pretty well so again. This is something that you can throw into your model at some particular point. But. Again you can see here the, frequency with which a, home, team wins a, match. 23, percent of the time they'll win it 1 nil, 2-1. They'll win 20 percent of the time if they do win at home so we're not saying that's the chance of them winning at home we're saying if they do win at home this. Is how they score of that particular match is distributed, so you can see basically. Here one nil to knit 2 1 to nil all occur, with a reasonable, level of frequency and that's, about that account for about 60%, of, just. Over 60% of all of the results. When a home team wins, so. We can go through the same process again we, use the different. Assumptions, that we've got here in terms. Of the. Chance that West Ham is is slightly lower than the average that we see in our data set and then. You can see here that it's basically saying, the. Chance of West. Ham winning 1 nil is about, eight point eight six percent or comes in around 11, so. I'm going to look at the correct score again I've, forgotten already what it was, so. Westham winning one nil is. Elevens so that's pretty much nailed to that, West, Ham winning. 2-1 is, around. 11th as well so you can see what's smiley higher on that so. You know maybe the two one, you, know that. Tells you a little bit as well because that's indicating, again, that. Perhaps the number of goals within this match is. Going to lead it to be skewed, towards, that end of the market and, if we look at two nil that they're coming in around the market is coming in around 18 so we're coming around 14 so a little bit shorter on that level. But. This you've got to remember this is quite a simplistic, model, so, we're, not looking at this model from the perspective, of being absolutely perfect, and there are tweaks and refinements, to be made you, can make those you can position.

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.

Could you share this excel spreadsheet? Cheers

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. :)


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.


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.

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