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Data Analytics in Soccer | Chalk Talk | 09.03.17

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and so let's get started because this is

what I want to talk to you about how we

use data analytics in soccer because

it's been a big boom over the last

stand' four or five years with data

analysis performance now is this

statistical analysis and specifically in

soccer I'm sure you've seen social media

Twitter everybody these logs are lots of

bloggers talking about how we use

statistics I will use data analytics but

I want to talk to you about how we use

it here at NYC FC and and basically I'm

gonna take you just talk to you a little

bit about the history of statistics and

data and where we're at now because

there's two types that we've used

physical data and technical data

I'm predominately predominantly on the

technical side but physical data is

where as a sport we feel like we've

mastered so a physical data I'm sure

you've heard of GPS so how far a play is

run what intensity is a player runner

how long does it play around for how

many accelerations how many

decelerations how many times that the

jump landed hit the floor

all this kind of physical data we feel

like we've mastered and that's been

through the use of GPS heart rate data

and I'll give you an example of how we

use it for instance if a player got

injured and had a muscular injury a

hamstring injury we could go back over

the period of time just before their

injury and we can look what did that

player actually do how long did he run

for what kind of physical law did he put

on his body that may have helped him

have that injury that injury occur we

can look at that time frame maybe six

seven eight weeks before we can see what

that player did and during our training

phases we can go back and we can see in

track okay we might be at a level where

this player could be at risk again so we

could manage display specific training

Lord during this week two week three

week period so from a physical

perspective we never learn we never hear

a Decatur chance of injury but we can

try and minimalize it and help this

player progress through a Pacific

training phase that he may have may come

have a Rico an injury try and prevent

that from the technical side of things

we have then

we're not would get into a stage where

we have access to lots of different data

has everybody heard of opto Sports yeah

opto no so for those who don't know opt

RA and

a statistical company who basically

watch our games and they called our

games just from a statistical

perspective they take information on

every single event on the field so a

pass a shot are throwing a tackle they

called everything and they produced the

data quantities quantitative

quantitative analysis and if everything

has happened on the field so that is the

type of data that I use and what we use

to produce a statistical analysis and

our data analysis as well and so for me

this is my definition of my role from a

dates perspective so it's bringing an

objectivity and a predictability to what

I believe is one of the most fluid

sports in the world I'm one of the most

opinionated sports in the world so I'm

sure everybody in the room today has an

opinion on how my safety played last

game where we good will be bad was there

somebody who was a player that was

particularly strong weak did we attack

well did we defend well everyone's got

an opinion and it's the same with fans

with everybody in the press in the media

with coaches with analysts with within

families people who support one team

another team everyone's got an opinion

so for me my role I see it's important

to bring an objectivity to kind of help

people's opinion support people's

opinions and see what has actually

happened from a different angle from a

very object see fungal so that's how I

see that's how I see my role what I can

produce and what I can bring from a data

perspective I'm from a video perspective

as well so statistics and analytics and

I want to talk about the differences is

there any statisticians are

mathematicians are who were anybody who

works in data analysis in the room yeah

can you tell me what you do can you

share what you do are can you share what

okay ask with know so for me I was from

a soccer perspective we have spent a lot

of time looking at statistics I'm not as

much time looking at analytics so you

may have seen social media at the end of

games there's lots of sort of passes

it's all successful passage how many

passes forward

how many shots how many and goals these

types of statistics for me we've not

looked at analytics and this is what the

main part of my talk my presentation

would be about the differences between

statistics and the differences between

analytics so from here this is from one

of our games this season and what in

this room what you think this data tells

us a below look at it look at it you

from yourselves what do your opinion

what what could have happened in this

game what could have gone on second

sorry very dominant CBO is very dominant

one quote would you agree with that yeah

is there anything else that you could

take from it apart from the overall

dominance of respective dominance of the

team say again so you can't tell who won

who did you think won the game seemed B

there's team a team a won the game yeah

so this is actually one of our games

this season and my next question is

which team do you think my CFC is Team B

seem be have you told him

that's gonna be my next question which

team is my CFC and if you're really good

which team is it that and we played

against and yes I London City when

played on my home first game of the

season okay so this was out this was

some of the basic data statistical

analysis that we did and by this game

the first game of the season that we

played and it's an example just a real

basic statistical overview of the game

so well done I'm really impressed didn't

think anybody will get that a far as

being really clever when I picked that

game okay so my next question is do we

need to question this data previously we

don't set a squad a game just an obvious

question do you think we need to

question this data I think we do because

we've lost the game but allegedly are

statistically we've been really really

dominant in every in every aspect of

being in possession out possession in

possession nearly 70 percent double the

passage apparently box entries nearly

double shot the shots on-target shots

inside the box 8 compared to this so we

still lost a game how did we lose the

game why did we lose a game and we're

going to question so I'm asking you your

opinion now can we make just looking at

this as a from an analyst perspective do

you think looking at this is enough to

make tactically help tactically improve

the team awesome help inform and make

need more historical data definitely any

No okay so yeah definitely something

didn't need more historical data just a

look back and see where we are like said

historical but for me just looking on a

very singular level these figures these

are variables just describe what has

happened there's no kind of underlying

theory there's no underlying explanation

we need to delve deeper into this we can

use it as a I was a good starting point

to see oK we've been dominant but how

what we use it as a gag as a GAD there's

no real kind of meaning meaning behind them

them

and that's how I see statistics and

that's why I feel as a sport soccer has

kind of we've been put back a couple of

years because we've been so focused on

statistics and they've been they've been

locked out so much in the media they've

been they've been built up so much I'm

really we needed to look at data

analytics and we needed to use these

variables to help do this like you

alluded to before create predictability

look it sit and get some more insight

into what is actually going on and

analytics for me is helping us do that

and we're scratching the surface we're

scratching the surface with what we're

doing at the minute and we're scratching

the surface but we're we're at a point

using the data that we have we're at a

point where we can now substantially

substantially say we can do this because

from an analytical perspective we have

the systems in place where every for

example every event that happens on the

field we can quantify and we can

quantify in relation to scoring and

conceding goals because ultimately

that's what we want to do as a team my

job is to try and help my team score

more goals than the opponent scores

against those when get three points so

every event on the field no matter if

it's a Shawn Johnson putting the ball

down for a goal kick and passing to the

right to Fred Breann or max you know our

our Joe Allen or whoever it might be

that has a quantitative and you can put

a quantitative value on that so what

Knights might happen going forward so if

you move into the attacking fear it

might be Maxie Morales beats to play a

1v1 and slides are through ball to WV

running into the box that might have a

bigger that will have a bigger value of

a pass

than the past from Shawn Johnson - it's

a fret so we're now starting to be able

to so quantify everything that we can do

and its effect and its influence on the

school play and subsequent possession

and this is the one value that we've

been looking at a lot this season has

anybody heard of this expected goals

you've heard of it what's your

you want to set the presentation sure

but that basically spot-on so expected

goals what is it he's hit the nail on

the head pretty much there so expected

goals look search shots and it looks at

up to coldest shots over a million

nearly a million a half shots in the

opto database and he looks at the

location on the field the type of shot

was it a header was it a volley was it

weak fought right foot left foot how did

it originate was it from a cross was it

from a free ball was it from a counter a

quick counter-attack a slow

counter-attack slow buildup play looks

at all these different variables and it

puts them together we get a regression

analysis so we look at the comparative

value of every the relate comparative

relationships between all these

variables and its effect and how do they

relate how do they compare and working

interact with each other through this

type of analysis to get an expected

goals so this means for every shot we

can assign a value how likely is that

shot or that attempt to a resort in a

goal our goal conceded why it doesn't do

though what it doesn't do and obviously

as analysts we need to look at the

limitations as well as everything that

we can do and we need to understand

these limitations and the limitations of

the expected goals is I'll give you an

example if Jack Harrison goes down the

right-hand side I'm puts across him with

his right foot although he's not really

likely to do it and and it goes right I

saw you jack it goes right across the

six yard box and there's nobody there in

front the goal to tap it into an empty

net because the goalkeepers dived than

the balls gone past him if nobody's

there and that shot doesn't happen

there's nobody there to take the shot we

can't we don't quantify that and qualify

that with an expected goals because a

shot didn't happen so that's one of the

limitations but for me it's not one of

the major ones as there's a reason

tactically behind there wasn't a player

in that position to have that shot okay

if that makes sense

well else it doesn't account for and

this is probably a major limitation for

me is the teammates defenders

goalkeepers the location of them who's

in front of the player when they've

taken the shot we're not looking at that

a player's their location at in in

position if I was to shoot and you were

to block it we're not taking that into

account okay which is probably a major

limitation but for what we have got on

what we do and how we Jets in how

reliable this and

and how substantial expected goals has

been we can't eradicate it but we're

taking it into account when were working

to make this a part of this okay so this

is the expected gulfs chart so I can

tell you in this blue zone here there's

a 33% chance of every shot to go in the

yellow zone 18% red zone 10% so what

does that mean everything outside of

that so every free-kick Andrea Pirlo was

scored in his career outside here you

probably should have passed

okay saying from from the from a run of

play it's gonna generally be a wasted

possession okay so you should look to

pass or create a further Avenue to

create an opportunity so cystic Leone

over 1.5 million shots okay

I'd let you find me let your own

opinions on that I believe it's quite

solid and I believe a lot of the chances

although and the emotion of the game it

kind of takes over you in these

situations okay was it the right

decision and when we get to a stage

which I'll talk about later we can

quantify or qualify and location and

look at that this I think will become

even more solid expensive goals and so

what do we get out of looking at

expected goals what do we get from this

from this distinct we get did we deserve

to win the game and I'll show you a

couple of graphs and a couple of tables

in a second

how is our team performing in attack

feed and in attack and in defense so how

are we for fine when we score how we

come performing from a defensive

perspective and this is a big one for me

because we can do all the work we want

from a statistical point which we can't

impact practice we can't impact training

we can't impact the 11 players that's it

and the 18 players in the squad that

take to the field every game day

then what's appointment okay so we need

to be able to influence and create

something where this analysis worked and

is it gone juices with how our coaches

work and can affect our players as well so

I'll go back to this little graph here

we decided that we can't really improve

performance directly just off looking

these metrics okay so from an expected

goals perspective from this same game we

were expected to count to have scored

one point one goal compared to our land

or cities not point nine one goal so

you're taking all their chances are

their shots everything we spoke about

here and they were only expected and

they were experts to score one goal we

were expected to constant one although

we've been dominant in every other

aspect of the game okay why is that so

this is a chance timeline so look at all

our attempts in blue and all our Landers

in yellow so this maps out in relation

to the time of the game where we've had

our chances and the value of each chance

that we have created our each shot we

have had the value of what's expected to

be to be a goal okay so you can see it's

chuckles to get to this point just over

fifty minutes to get to a similar level

of our land or chance the chance they've

created sixty percent chance of scoring

okay and over the course of the game it

took us over eighty minutes when you

look at how dominant we were they still

caused eighteen minutes to get to the

same level that Isle and those chances

were although we've had we control

possession from possessions that we've

been in there half half the amount of

time with the ball then we did without

it they stalk was 18 minutes to get

there so this provoked further further

and further questioning we take at this

we can use this as a good guide of the

periods where we've been quite dominant

but we create the chances that haven't

been that high in quality should we say

so this next one

now we're looking at where our shots

were from so relative if you remember

the map I showed you with the circles of

the blue red and yellow zones looking at

its actual chance we've had all inside

the box these are the eight shots

compared to all angles one chance and

how good this chance was for them to

score but as you can see we've had one

shot inside the Blue Zone where she's

fifty percent chance of scoring we've

had free shots inside the yellow zone

which is fifty four percent chance of

scoring saw them to them attempts

combined probably gives us that goal we

should have scored tatata game so we

created them similar chances there and

would think okay we can look at them

chances in a little bit more data how

did we create them chances why did we

not score outside of that we had four in

a red zone

but she's for any chance of scoring but

obviously we need to look at them in a

little bit more detail as well so now

looking at everything combined our

expected goals knowing what with

understanding my expected goals and how

we formulate and calculate is looking at

the chance timeline and the types of

chances we've conceded there can we

start to make a few tactical decisions

now I can we start to inform the process

yes no what do you think we're going

into a little bit more detail and we can

quantify a little bit better and I have

a better understanding of the types of

chances we've created in relation to our

dominance can we now start to formulate

that's that's working say that again sorry

yeah yes so now we have a better

understanding we have a better and we're

better educated in what we need to do

where we want to go how we want to do

that yeah okay definitely a hundred

percent but again for me tactically we

still can't really influence we still

can't really influence from this we have

a better deeper understanding and it's

pointing us in a direction and it's

pointed us in a direction where we can

do this start reviewing specific footage

so it's giving us a real form the

standing of where our chances have come

from the quality of them chances and

pointers in the direction of the areas

relative to the timeline where we need

to look how we need to look okay so we

go back and we review this and then this

would work on specific technical and

tactical content during training session

so that was pointers in that direction

to use the video to then see okay so

these chants would be created which had

first three percent chance of have been

converted what kind of chance was it and

how did we create that chance in the

context of that game so then we can look

at that chance was there ways that we

could create better chances or how did

we create that chance and then we look

at the other the chances that weren't

maybe so good and why was that why was

that so it's provoking us and looking

out in a different perspective so this

is where we are now in relation to

expected goals so expected goals in the

league we have the highest highest in

MLS so about of all the chances we've

created while the analysis that we've

looked up after all the chances during

the season we've gone through we've had

a lot will seem where I'm gonna tell you

out because I've given a lot of secrets

away but we've looked at how we create

our chances and now we're just above

Chicago in terms of goals up we have

expected to score which is for less than

what we have actually score

so we're outperforming what we are

predicted to be and we're supposed to be

this season but using this data so

pointers in the directions to guide us

and to inform the coaches and we've been

able to structure

training we've been able to use it as a

guiding principle we're able to use it

to to guide our focus on

goalscoring and and defendant and I

really on a really basic level and our

coaches take this information really

really well so come up with decisions

like this but we're using their

expertise from a soccer playing

perspective and coaching perspective

mixed with their data to come up with an

object if you point in a standpoint of

where to focus our attention because we

want to focus our attention on the plays

that are going to scores more goals and

stop us conceding some really obvious

level we want to focus on that and this

and the data is giving us that direction

to be able to focus as well as taking on

board the subjective feelings of the

coaches from a tactical perspective it's

really really important because they

have really good experience great

experience in the game they know the

game really really well their intuition

is not something that as data analyst

there's a farm science what we can

ignore notion no way because their

intuition and what they feel about the

game how they see the game is really

really important but we from an

objective standpoint need to use this to

help okay is this correct if it's not

how can we look at a way of saying okay

from their Jets if some point this is

what we need to do this is how we need

to do it okay then you come together so

it's a really really strong working

relationship one myself daily have with

the cultures of their of my CFC so just

to finish data analytics and we're next

so alluded to it before with location of

players and being able to map from a

technical standpoint where they are on

the field and how their relationships

integrate with each other and how my

movement five yards forward could

influence your movement five yards

backwards how does that in the middle in

and rating a goal scoring opportunity

and this is XY coordinate data location

data is what we're calling it this

company's core track up

trial in in the Premier League this type

of data so we're on the cusp of it to be

able to really solidify everything that

we do from data perspective and to be

having a little bit more predictive

analysis or what could actually happen

so try and map it out from the real

objective standpoint of what is going on

from a numbers perspective when we get

this XY data it's going to be really

really exciting for the sport and you'll

see a massive boom and a massive change

in how data analytics is perceived in

the outside wider community so we're

still we're looking for this and then

and last but not started on it yet but

as a group CFG from a research

perspective we're leading the way in

Europe and in England on trying to get

this and push this forward and try out

trial in different algorithms trial in

different software companies just to see

where we are at with this the location

data because once we get that from a

technical perspective and comparing the

interactions of every single player on

the field and have imagined an Excel

data sheet with millions and millions

and millions the roles being able to go

through that on how each players

movement has affected the next place

then we really start to see the game

from a tactical perspective and it

really starts to relate to the numbers

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