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

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. 🙂

Could you share this excel spreadsheet? Cheers

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

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

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

You spoke all day and said nothing

Great video. Explained a lot.

HELPFUL VIDEO SIR.

WHAT ABOUT WIN OR DRAW?

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

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''

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?

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

Money talks, BS walks

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?

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

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