September 10, 2019 16

Football betting tips – Predicting correct score odds

Football betting tips – Predicting correct score odds


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
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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

16 Replies to “Football betting tips – Predicting correct score odds”

  • John T says:

    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. 🙂

  • Alexis García says:

    Could you share this excel spreadsheet? Cheers

  • Philip Brown says:

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

  • Matias Pedersen says:

    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

  • Raphael Albino says:

    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

  • Tony Day says:

    You spoke all day and said nothing

  • Gary Carlyle says:

    Great video. Explained a lot.

  • vialo mann says:

    HELPFUL VIDEO SIR.

  • vialo mann says:

    WHAT ABOUT WIN OR DRAW?

  • Shokotoko says:

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

  • 1faustus says:

    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''

  • Aftershock says:

    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?

  • David Kostadinov says:

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

  • Melissa Johnsson says:

    Money talks, BS walks

  • steve dung says:

    Nice

  • Adrian Constantin says:

    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?

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