Join Catapult’s Nichol Forbes as he talks through how to make use of Maximal Intensity Intervals in the OpenField Cloud and Console.
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In this video, I’m going to be looking at

the max intensity intervals in OpenField, cloud and console

and talking about how we can view the most

demanding passages of games or your individual periods.

So to start with, I’ve created a new dashboard and

I’m just going to use the wizard to create a

table that’s going to look at the max intensity interval.

So I’ll click into table here, and then once

we get into the parameters section, you can scroll

down to find maximum intensity intervals here and we

can add the values or the parameters for interval

one, interval two, and interval three for distance.

I’ll talk about what these refer to in

a moment, but please note you can also

get this data for player load as well.

You will also see if you want to look at the start

and end time of the, of each of these intervals as well.

You can put this information in as well,

but for the time being, we’re just going

to look at the intervals themselves.

So I’ll click next and create, and finish

creating this table for the maximum intensity intervals.

And once this comes out, you’ll see this kind

of information where we can see the distance and

player load information for each of the intervals.

And what each of the intervals refer to

is the amount of time that has passed

for each, for each moment of the game.

So interval one refers to every 1 minute,

interval two refers to every three minutes, and

interval three refers to every five minutes.

Now, you can rename the parameters if you prefer,

rather than saying interval one, two and three.

You could put it as 1 minute, three

minutes and five minutes if you wish to.

Also, you can change this.

This isn’t a mandatory field.

You can change the amount of time that

is housed in, within each, within each interval.

And I’ll come on to later and show you how

we can do that at the end of the video.

But essentially this is how you can view the

data in the OpenField cloud and if you wanted

to look at the information for one particular period.

So for example, if I was to click

on the small sided game, you’ll see that

the data will change based on when that

players individual maximum intensity interval was achieved.

Okay, so that gives a good summary of how

we can view the data for an entire activity

or for a specific period or half.

Now you can take this information even further if

you want, by coming into the OpenField console.

And if you want to, to look at

other parameters that happen within that, within that

time period of the maximum intensity intervals.

You can find it a lot

more easily in the OpenField console.

For example, what you can see here is I’ve

got a table set up that’s going to look

at and analyze the maximum intensity intervals.

If you want to create this table, all you

need to do is just right click into the

area at the bottom and search for events and

efforts here and then go for maximum intensity intervals.

You have the option, as I said

before, for distance and player load.

So you can click one of them to create this table.

This particular one is looking at distance and as you

can see you get the distance value for the session,

or at least for the maximum intensity interval portion, as

well as the maximum velocity that was achieved during that

time and what you will also see as well.

So I’ll just actually click into

the small sided game part two.

You will see that the data will also

change depending upon when, what period was selected

and when that interval was achieved.

But in this example, I’m just going to

look at the entire session as a whole.

And what we can see from player

eleven is that he achieved his maximum

intensity interval during the medium sighted game.

Now we can take this a step further and if

I actually click on this time, you will see that

the cursor has moved to that portion of the training.

So we can see that for the 1 minute, for

the three minutes here and for the five minutes I’ll

use the three minutes for the time being.

And what we can do from here is create

an annotation that’s going to create kind of a

dummy period that we will use to find out

more information about this, about this time here.

So to create this annotation, I’m just

going to right click into our activity,

come down to add annotation layer.

And now what we can do is we can hold

Control and T over the area that the maximum intensity

interval started and bring it across until it ends.

You can see the time in the background

and I know this lasted for around, until

around 15, until around 1524, like that.

And after creating, if the player was

selected in the active players, you’ll see

that he becomes pipped into this annotation.

Now what the annotation does is it means you can

see the data in the annotation without having to pip

him into a new period or anything like that.

Also when you sync this to the cloud, you

can still see the data for the overall period

and separately in a different table, you can see

the data for the annotation as well.

But what I’m going to do is I’m just

going to click into the activity and then click

back into the annotation we just created.

And then I’m going to create a new table and

that’s going to look at some of our other information

that we want to see about this, about this parameter.

So I’ve just finished creating the

table, as you can see.

And what we can see here is some of

the information that comes through from the annotation.

So I’ve chosen to look at the

likes of high speed distance and distance

per minute across this three minute period.

And we can find out any other information as

well by going to the widget settings wheel and

choosing the parameter you want from this list here.

But that would be a good way to

view the data in this three minute period

to see what his high speed running was.

For example, information we can’t

find in the table above.

Now, if you’ve created, if you’ve created your annotation

and you see that it’s slightly not at that

three minute mark, or maybe you want to change

the name of it, you can edit the annotation

similar to how you would with a normal period.

So if I go to edit, I can edit the time here

to make sure that it’s definitely looking at every three minutes.

And as I said, we can also change the name.

So I’m going to call this

MII three minutes player level.

And that would be a very good way to

view that data for the annotation for this individual.

As I said, you can sync this into the cloud

and view the annotations in the cloud as well.

However, this would be a very good way

if you want to do some further research

about what the metabolic power is.

For example, within this time period,

you can see that information here.

Finally, I just want to show you

how you can change the intervals.

So if you didn’t want to have it

as 1 minute, three minutes and five minutes,

you can change these intervals if you wish.

So all you need to do is come to

the OpenField cloud, come to your teams section, and

then go to the selected team that you want.

So in this case, I’m going to be using Catapult Sport.

Then if I scroll down so far, you’ll

find that you have the options here for

maximum intensity intervals, distance one, distance two and

distance three, and the same for player load.

Bear in mind that these are in seconds.

So if you wanted to change it to, let’s say

you wanted to change it so it was at 1

minute, two minutes and three minutes for each of the

intervals, we would have to change this by the seconds.

So we’d have 60 seconds, 120 seconds, and 180 seconds.

After that, just press save changes and those

new intervals will be applied in your account.

Join Catapult’s Laura Davies as she talks through how users can add average sets and make use of them in the Comparison tab within widgets.

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Hi everyone.

My name is Laura Davis and I’m

a Customer Success Specialist here at Catapult.

Today I’m going to be answering a

question we’ve had submitted through our Catapult

unleash success and support portal.

So let’s jump straight to it.

So the question I received was how do

I add comparisons to my OpenField cloud reports?

So this obviously includes comparing your

sessions to your match day values.

This could be comparing your most recent

match day to your match day averages.

It could be comparing your sessions to day of the week.

That could be matchday minus one,

minus two, matchday plus one.

It could be looking at your averages of

your particular periods or drills and so on.

So very simple to very, very simple to do.

We’re going to head straight to our

settings section here in the cloud.

We’re going to navigate to our

averages on the left hand side.

So this pops up all of my average

sets that I’ve already previously created here is

where you can create all of your comparisons.

So to create one, I’m going to

go to generate new average set here.

This is where it all happens.

So it’s really important to understand what

type of average you want to build.

There’s four options.

So you can build an activity average,

one that’s based off your athletes, a

certain position or a specific team.

And for the purpose of this video, I’m

going to create one based on my team.

I’m going to do it for all teams.

You can select a single one if you wanted

to, but I’m going to do all teams.

Step two, building an average.

My team four and I want to look at game data.

So I’m going to hit this button here.

I’ve got a lot of options but because I want

to look at my game day data, I’m going to

scroll down and I’m going to go to the tagging

section and I’m going to utilize the day code tags.

So I’ve selected day code tags and

I put the equal sign here.

So it’s going to equal game.

So that’s the tag that I want.

I’m going to add that.

So that’s my building average for my team.

Based off of all of my activities that have

the tag game for day code period data for

data source parameter group, I’m going to build the

average for all of my parameters.

But you can go a little bit more specific.

If you’ve previously created a group, then

I’m going to call this game perfect.

Once you’re happy, generate this will create it and

then once done, head to your timeline and you

can go back to your report on the cloud.

There we go.

So this is my game report and I’m

going to start adding in my comparisons.

So here I’m going to have a look

at my average acceleration efforts, for example.

So if I hit the settings cog here, this

will open up a new window and I’m going

to head to the comparison tab here.

So I’m going to select my game

average average set which we just created.

I leave it as total for now.

I’m going to tick the box down

the bottom which goes allow advanced comparison.

I’m going to hit. Done.

So what this is showing me is my average game day data.

My team performs 22 acceleration bands, one

to three efforts for a session.

And then looking at the number at the

top for this session we’ve done 58.

So it’s been quite an increase.

But, but how much of an increase?

So if we navigate back to the settings cog comparison, if

I go to the drop down next to the average set

that I selected, I’ve actually got the option here.

So I can have a look

at total difference percentage change.

Just for the purpose of this, we’ll

have a look at total difference done.

And we can see in comparison to the average of their

match day data, there’s been an increase of 35 efforts.

And then if we go to details we

can do the exact same thing here.

So comparison game average.

Now we’ll have a look at the

percentage change compared to the average.

Make sure this box here is ticked on the bottom hit.

Done.

There’s actually been 140% increase

compared to their average.

I want a little bit more context comparison.

Hey, what was the actual percentage?

There we go.

So you’ve got a couple of options there

of how you want to be the data.

You’ve even got Z scores in here as well.

And obviously you can see at the moment that all

of my percentages and values are appearing in green.

This can be customized.

So if we go to display settings at the

top here, you can see how the percentages are

set and the color that’s assigned to them.

So you can change it.

Just click on the color box, pick a color,

and then again you can change the values by

just clicking in the box and typing away.

That’s done.

So it’s very, very simple again, for average

distance here at the moment, my team average

set won’t work because for this one here,

my row label is based off an activity.

So if I go to the comparison game average total

comparison it’s giving me a number which is incorrect.

So because it’s a team based average, I need

to make sure my row label reflects that.

So I’m going to change activities to team

and they should give me the correct data.

Much more realistic.

So on average my team is doing three point three

k and on this one occasion they have reached 5.9.

So yeah, so that is how

we add comparisons to our reports.

Just to recap, we go to settings and then averages.

There you can create your average sets

depending on if you want it built

on activity, team position or athlete based.

Once created, you can come to here, make sure

the data you’re viewing reflects the type of average.

So if it’s a team based average, you’re

viewing the data as a team and then

it’s a selecting the settings cog and utilizing

that comparison tab and that’s pretty much it.

So I want to thank you all for listening

and feel free to raise any questions you have

via our Catapult Unleash platform and I look forward

to speaking to you all soon. Thank you very much.

Join Catapult’s Lori Swartz as she talks through how users can update an athletes position history and filter widgets in order to create position specific reports.

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Hi, I’m Lori Swartz, a

Customer Success specialist at Catapult.

In today’s video, I’ll show you how to

update an athlete’s position history, filter widgets by

position, and visualize position specific reports.

So let’s dive in.

Changing an athlete’s position might be necessary for accurate

metrics in open fields, such as for goalies.

Or maybe you just need to

change someone’s position for whatever reason.

To do this, you would go to settings at the

top, make sure you’re under athletes on the left hand.

And then we would select edit for whichever

athletes we need to adjust this for.

I’m going to pick the first one.

Then we would hit edit history.

And here we see their

current position is defensive end.

If we wanted to edit this current position, we

would just hit edit on the far right.

And then we could change either their position or

specify a specific end date for this position.

But you could just keep it at current and I’m going

to change them to a defensive line hit edit entry.

Now, their position here was edited

and changed to a defensive line.

After you change anyone’s position history or position, you

want to make sure you do a full sync.

Solidify those changes.

If you want to add a new position, you

would hit under add entry the start date, say

today, and they’re going to go back to a

defensive end and I’m going to hit add entry.

So the defensive line ends August 5.

This new position starts today

and is their current position.

And again, make sure you do a full

sync after just to solidify those changes.

Next, let’s create a position report, um, and

kind of show some different ways that you

can visualize, um, positions as well as learn

the capability to filter for specific positions.

So let’s create a graph.

In this graph, I’m just going to show

the player load for each positions for this

practice and render this as a chart.

And I’m going to make my first row label positions.

Fun fact, if you right click in the selected

parameters and hit select all, then right click again

and then remove all selected, it gets rid of

everything all at once for you.

I’m going to hit done.

So now we have a graph of each

position average player load for this activity.

If I want to edit it, I would

go to the settings, go to options.

I want to show some data labels.

Maybe I want to make it a different direction.

I go to series, I can edit it further.

I’m going to change this color to blue.

Then you can also do a z

score, trend line or an average hit. Done.

So here’s a simple graph.

Next, let’s create a table.

So I’m going to create another widget and I’m

going to do a, let’s just say position averages.

And again, the row label is going to be positions.

And let’s create a table for player load.

All right, so here I just created so we

can get some averages for each position group from

this activity, for some random, for some metrics here

we can dive into this a little bit further.

If I go to the widget settings and go to

table options, I can do an average here where it’s

going to show an average at the bottom.

You can select these other options as well.

And if you wanted to, another way to view things.

If we did position and then made the second row label

athletes, what this is going to do is show each athlete

in each position group and then show them you.

The average of each athlete

needed all those position groups.

Just another way to visualize it.

Alright, let’s stay here.

All right, let’s create another graph.

I’m just going to copy this.

Let’s create a graph of individual player load

and player load for minute for each athlete.

I’m going to sort it by position name

and make this a little bit thicker.

And let’s add player load per minute.

Done.

Edit this a little bit more.

Go to options swap.

And I want to make player load

per minute orange and either align.

I’m going to go with a spline.

I like those a little bit more.

So here we have each athlete’s player load

and their player load permitted from this activity.

And it is sorted by position group, which is how

I’m able to have the positions pop up there.

If I were to sort by

their first name, that will disappear.

So if you wanted that view, you

would sort by their position name.

So let’s start to work on some filtered widgets.

I’m just going to create something to give

us a little bit of a break between.

So I’m just going to do a title box.

So this is a football team.

So let’s create some widgets filtered for offense.

So first I’m going to create a table.

I’m just going to copy this and let’s do.

So I’m just going to name

it with the positions in here.

Just so I don’t forget.

Receivers go back.

Alright, I’m going to make it a little bit bigger.

And eight by, I don’t know, six

and I’m going to go by positions.

And then athletes, I’m going

to keep the same parameters.

Now I’m going to go into the filters.

So here I’m going to hit the

drop down where it says activity.

And then I’m going to filter for position.

I find it equals the carrots facing away.

That’s exclude.

So I want to equal

wide receivers, quarterbacks, signals, offense.

Then after you select them, you want

to hit add, filter and then done.

So here now I have each position group, that’s

offense only and each player in those position groups.

Now you see it says one filter applied.

If I hit that I can hit edit.

And if I didn’t want quarterbacks in there,

I could exit out, update, filter, hit, done.

Now you see quarterbacks are gone, but

I’m going to put them back.

So let’s create a graph.

Gonna copy this.

So let’s do, let’s do total

player load for each player.

So I’m going to change this to athletes instead of.

I could have kept average but that’s okay.

Total player load filters.

I’m going to do position

equals receiver, add, filter, done.

I’m going to edit this a little bit further.

Go to options.

I’m going to swap them.

Then I’m also going to sort by position name.

And if I go to series I’m going to add a z score.

So now we have the offense

for each player that is offense.

And we have it sorted by position and

what their player level was for this day.

Let’s do one more of these.

Let’s do total explosive efforts.

So I just have to change the metric.

Nothing needs to change in the filters.

It done.

And if I go back to the widget settings

and series, I’m going to make this one orange.

And again you can add a z score.

I’m just going to put an average.

So we have that one there.

Let’s dive in a little bit more to

start messing with some other types of filters.

So let’s do their player load from

the last 14 days or two weeks.

I’m going to do the team average, I’m sorry,

the offense average from the last 14 days.

So I’m going to make this first row label activities.

You could also make it say date, but I’m just

going to put activities instead of total player load.

We’re going to need average player load.

I’m going to keep my offense filters, but

now I’m going to do last activities.

You could do last days equals 14, but

I’m going to do last activities equals 14.

Hit, add, filter.

Done.

And I forgot to increase the size and I’m

also going to make this a different color and

a green and I’ll do a z score.

So here we have the offense player

load from the last 14 days.

We can do something similar but show

the, let’s say an athlete’s accumulated player

load from the last four days.

So I’m sorry, let’s do last seven days.

So this time I’m going to do athletes.

I could keep.

I’ll show you this in a few different ways.

We can say average player load.

I’m going to edit the last activities,

say seven update, filter, hit, done.

Go to the widget settings series.

I lied.

Settings, basic activities, hit, done.

It’s going to look crazy and then I’m going to go

to options stacking and then I’m going to hide my legend.

So here we can see each player’s accumulated

player load and the activities that created that

player load from the last seven activities.

Another way you can view this for the athletes is

I can get rid of this activities in the second

row label have this say total player load, same thing.

Except now we just don’t know.

We just don’t see where the

volume is coming from specifically.

Let’s just do one more report.

Let’s do a weekly accumulation for offense.

So weekly accumulation I’m going to make this.

You could do week of the year or a week in season.

If your account is set up for a week in

season or has a season start date, I recommend that

I’m going to do a week of the year and

then I’m going to make the second row label activities.

We’re going to do something similar

to what we did above.

I’m going to do average player load and

instead of the last seven activities I’m going

to change this to the last 28.

Another option would be instead of doing last activities equals

28, you could hit this drop down, do a time

range and say starting from, let’s say pretend camp started

on the 29th and I can add an end date.

So filter for any activities within this time range.

Or I could leave the end date open

so that this would continue to update.

So I could say last activities starting from that

time and push the last 200 activities through.

That doesn’t matter.

It’s still only going to start from July 29 and

update with whatever activities I upload after July 29.

But now instead I’m just going

to do last activities equals 28.

So I’m just going to push

last 28 activities through this filter.

So yours might have came up with a legend and it

might show up unstacked so it might show up like this.

All you need to do is go back to

the options, stack them, and then remove the legend.

So now we see the weekly accumulation in player

load for the offense team from week to week.

Join Catapult’s Jozef Baker as he talks through how users can configure and see High Speed Distance live on iPad.

Running a Live Activity Guide
Catapult Vector Application

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Okay, so we’ve got a question here from one

of our Vector Core users asking how can I

see High Speed Distance Live on my iPad?

Well, a couple of things that we need to check first.

So the first thing is to check

that you’re using the correct application.

This should be the Catapult Vector app.

I will provide a link to that

beneath this video for your reference.

But it is the app in the App Store that

has the word ‘Vector’ in the app icon itself.

The next thing you’re going to do is, in your

cloud software, I want you to navigate up to the

Settings menu and then into the Bands menu.

And then from the drop down select Velocity.

High speed Distance will be taken from

distance accumulated in Bands 5 and 6.

So in this example here, this would be any

distance accumulated above 5.5 meters/second once you’ve checked that

and you’re happy and you’ve applied any thresholds that

you’ve changed to the rest of your athletes.

If you actually navigate into the application itself

beforehand, be sure to kind of sync any changes down.

Start a New Session and create a

period as you would do normally.

Add any athletes into that period.

Then what you’re going to do to see High Speed Distance

is you’re just going to scroll across the top here.

So where it says Total Distance, just place one finger

on the iPad screen itself and just drag right to

left and you should see High Speed Distance appear.

What you can also do as well, if you

click on the Target View in the top right,

you can set a target for High Speed Distance.

Either drag or toggle the slider or

enter it on the right hand side.

Once you’ve set a target, just toggle the Target Mode

on to see it as a percentage of your target.

Hopefully that answers your question.

Please do continue to check in and

engage with us on the Unleash platform.

We’ll see you on the next one.

Join Catapult’s Jozef Baker as he talks through how users can import Focus tags into OpenField and report on them as an annotation layer.

____________________________________

Okay, so another question here from one of our

Pro Video and Vector Core Plus users asking; How

do I import Focus Tags into OpenField?

Okay, so to answer this one we’ll dive

into both Focus and OpenField and hopefully I

can give you the answer you need.

Okay, so as you can see here, we’ve

got my Focus package which I’ve created.

I’ve gone through and created tags for each instance.

And importantly I’ve actually tagged the start of the

first half and the start of the second half.

And I’ve been careful to name that

tag Start with a capital ‘S’.

The next thing I’m going to do rather than going to

export session JSON is I’m going to go up to the

top here and I’m going to select export session XML.

So just press ok on that.

Choose a suitable save location then.

Now what we’re going to do is we’re

going to navigate across into the OpenField Console.

If you right click on the top of the OpenField

activity, navigate down to import codes, Hudl Sportscode annotations

and you’re going to select classic at the top, press

select file and go and navigate and find the xml

that we’ve just saved from Focus.

Now you should be presented with all of the

tags that you’ve created in your Focus package.

What you’re going to do is just highlight all of those.

Do not select the or check the event box

and remember to select the start tag as well.

If you navigate up to the top,

click align, start with periods and just

select the two OpenField periods there.

When you’re happy you can press import.

Once you’ve done that, you should see that we’ve

got start tags lining up with the first half.

And then if we just drag move to the right here and

the second of those start tags will be in line with the

second half and all of our tags will be in the console.

So all you’ll need to do there is just complete a.

I normally recommend doing a Full Bake and then

following that make sure you do a Full Sync.

So now if we go into the OpenField

Cloud, you’ll see we’ve got all of those

tags that we created in Focus that we’ve

subsequently loaded into the OpenField Console appearing in

the timeline here I have created some widgets.

So if I go into one of those, the widget

here under source, you need to make sure that you

select ‘Annotations Database’ as opposed to ‘Periods Database’.

And then you’ve got the option to choose

from Annotation Category in the row labels.

So as you can see here, we’ve got total

distance for each of the tag categories and I’ve

displayed that as a chart on the right side.

If you want to show data for individual tags

again, you’ll need to change the source to Annotations

Database, but in the row labels you’re going to

select Annotation Name as opposed to Category.

One thing I recommend doing if you’re going to

select individual tags is toggling how you sort those.

So next to the row label, I would recommend

choosing to sort by Annotation Time and as such

you’ll see that they’ll go in chronological order.

So here I can quickly see that this Team

B Positional Attacks With Chances had 1230 meters on

the right hand side here I’ve just displayed the

same information in a chart and this is probably

an easier way to pick out those peaks.

Okay, I hope that answers your question.

Do continue to check in with the Unleash platform.

If you have more questions, feel free to

go through the Success & Support portal.

Join Catapult’s Jozef Baker as he talks through how users can create high speed distance metrics above a percentage of an individual’s maximum velocity.

____________________________________

Okay, we’ve got a question here from one of our Vector

Core Plus users asking how do I create a high speed

yardage metric above 80% of an individual player’s max?

In order to answer the question, what I’m going

to do is we’re going to jump into the

OpenField Cloud Software and walk you through the process.

The first thing you’re going to want to

do is go up to the Settings menu.

Within settings, go into the Athletes tab.

And if you aren’t already in this Grid View,

just toggle between Grid View and List View. Okay.

The reason being here is you can edit

multiple players at once in the Grid View.

So what I’m going to do here is

just enter in the Max Velocity on the

right hand side in your units of measurement.

The next thing I’m going to do is just head over

to the Bands section and then I’ve either got the option

to go into Velocity Bands or Velocity (Set 2).

You should have the option of both.

If not, do let us know.

We can add that into your account.

What you’re then going to do is change

the units of measurement and then you’re going

to toggle the Use Percentage button on.

Once you’ve done that, these bands at the top

here, you can, you can change, you can edit.

And as we can see here, I’ve already got my

Band 5 low threshold set to 80% because we’re using

the Use Percentage mode of the player’s maximum.

And that maximum will be taken from the

maximum you set in the Athlete Profile.

Okay, so if we head over to Parameters now

and just scroll to the bottom because I’ve used

Velocity (Set 2), I’m going to show you how

that looks in Velocity (Set 2).

Now if you are looking at Minimum Effort Distance or

Total Efforts, you’ll be able to see here that the

Velocity Band will have a ‘+’ after it.

So either Velocity Band 5+ and that will

count kind of all efforts above that band.

So in the example we’ve been working on here, we’ve

used Band 5, but we also had Band 6.

That Band 5+ would be inclusive of the

Band 6. If you’re using something like Distance,

So if we scroll down there we go

Velocity Band 5 Total Distance (Set 2).

We would also probably want to include any distance that

we accumulate in Velocity Band 6 (Set 2) as well.

So to do that, we can go into List

Custom Parameters and I can Add Custom Parameter.

So what I’m going to do is just call

that custom parameter >80% of Individual Maximum Speed

in the calculation if you just start typing.

Even if you type the first letter of each of

the words, you should get some predicted metrics and just

select the appropriate so Velocity Band 5 Total Distance (Set

2) plus Velocity Band 6 Total Distance (Set 2).

Once I’ve selected both of those, I can Add

Parameter and that will be available for you to

make use of in an OpenField widget.

I hope that answers your question.

Please check in with the Unleash

platform for more content like this.

Join Catapult’s Ross Goodall as he talks through how teams approach weekly periodisation in association football, Soccer.

____________________________________

So the question I’ll be answering today is; “How

much shall I load my players and when?”

The periodized week should be planned around how coaches

are wanting to structure their training weeks, when players

are in and when they’re off recovering.

It’s the role of a sports scientist to

balance exposing players to enough training load to

increase their fitness while at the same time

being careful not to overload players.

The values I’m about to talk through are

shown as an approximate percentage of match loads.

The first model is the Traditional Model.

This is a model in a typical one game

week schedule and gives players two days off.

This model prioritizes freshness in the

lead up to the match day.

However, this may not provide enough recovery time.

Research suggests players should have at least

48 hours post-match date for recovery.

Athletes will also only have

one conditioning day per week.

Model number two is the European Model.

This is a model which has been

adopted by Rafa Benitez in the past.

Players are back in on match day plus one (MD+1) for

active recovery and squad players will receive top up sessions.

Match day plus two (MD+2) will then be a rest day.

This model generally prioritizes recovery.

There are long lead times into games

with one day dedicated to conditioning.

Model number three is the Tactical Model.

The Tactical Model has been used by Jose

Mourinho and his staff in a very immersive

approach to where there are no days off.

This affords the coaches two days of conditioning,

one dedicated to small spaces with lots of

Accels and Decels and one with larger

spaces with more high speed running.

Lastly, we have the Adapted Tactical Model which

has been used by Pochettino in the past.

This is similar to the Tactical approach,

but swaps the extensive and intensive days

around and instead incorporates a friendly game.

This is used to simulate two day game weeks, but

once again there’s little to no time off with family.

Thanks for listening, I hope you found that useful.

Continue to check in with the Unleash

platform for more content like this.

Join Catapult’s Connor Howley as he talks through how long it takes soccer players to recover after matches.

____________________________________

Hi, my name is Connor Howley and I’m a

product support technician here at Catapult who is going

to take you through a query surrounding football recovery.

So we’ve got a question here from one

of our users, which is; How long does

it take players to recover post match?

So Silva et al in 2018 conducted a systematic

review looking at the effect size of performance

markers at different intervals, which were during an

dematches, and they compared these against baseline measures.

They found a large effect size looking

at creatine kinase and delayed onset muscle

soreness 24 hours post-match.

They also found moderate effect sizes when looking

at hamstring strength and counter movement jump height

48 hours post-match, there was still a moderate

effect on hamstring strength, creatine kinase and delayed

onset muscle soreness, and also small effect sizes

were seen in quadricep strength, linear sprint performance

and countermovement jump performance.

72 hours postmatch, there was still a

moderate effect size in hamstring strength and

countermovement jump performance, while the remaining

performance markers showed as small or trivial.

As Silva et al. states

while some parameters are fully recovered,

a 72 hours period is still not

long enough for complete homeostasis.

Generally in sports like association football, it is unlikely

that players will ever receive this long to recover

before preparation for the next game starts.

As such, coaches must adjust structure and content

of sessions in a 72 hours window to

respect recovery while ramping towards conditioning sessions.

Thanks for listening, I hope you found that

useful and please continue to check in with

the Unleash platform for more content like this.

I’ll see you next time.

Join Catapult’s Alex Lowthorpe as he talks through why top up sessions are required for soccer player and when they can be delivered.

____________________________________

So, we have a question here from one of our users.

Do I need to give extra work to the players

who didn’t play the match and if so, when?

Before we dive into the answers, let’s

look at some of the research.

Anderson et al.

Looked at squad status and the implications

for physical load throughout the season.

He defined starters as those who started greater than

60% of games, fringe players as those who started

30% to 60% of games, and non-starters as those

who started less than 30% of the games.

Looking at the differences between starters and

non-starters across training and matches, he found

the effect size was very large across

running high speed running and sprinting intensities.

When looking at running intensity, starters covered

approximately 92 km per season compared to 58 km for non-starters.

For high speed running, starters covered approximately

35 km per season compared to 19 km for non-starters and

for sprinting, starters covered approximately 11 km per season

compared to 3 km for non-starters.

When comparing starters versus fringe players, the same

very large effect size was apparent in sprinting.

Starting players completed approximately 11 km compared

to fringe players who completed approximately 5 km of

sprinting distance per season, indicating that unlike

total seasonal volume of training, I. E.

Total distance and duration, seasonal high

intensity loading patterns are dependent upon

players match starting status.

Therefore, there is a requirement to address this through

training exposure. To answer the first part of the

question, do I need to give extra work to

the players who didn’t play the match?

The answer is yes, but when?

Well, if we look at a typical week,

we have three opportunities before the next team

conditioning session, the first one being match day.

After the game.

It is not uncommon to see substitutes and

players who did not enter onto the pitch

receive high speed and sprinting top ups.

This is often preferred by players and staff as their

schedules match with days off at the same time.

The next option is to split it over

match day and match day plus one (MD+1).

However, this means players have no days off.

Alternatively, another option is for these players to complete

their extra work on match day plus one (MD+1).

However, this doesn’t give the players the best

chance to recover and perform at their best

during training on a match day plus two (MD+2).

Thank you for listening.

I hope you found that useful.

Join Catapult’s Sophie Goves as she talks through what the research suggests for senior and junior female soccer players.

____________________________________

We’ve got a question here from one

of our users working in Women’s football.

They’ve asked which value should I set

as my speed and velocity thresholds?

Let’s jump into it.

Before you decide which thresholds to use,

you need to understand whether you use

absolute or relative bands in zones.

Absolute bands are used with

more of a performance outlook.

Relative bands are more often than not

used as part of development outlook.

If we look at absolute values, the best place

to start is probably what is used by the

governing body in 2019 FIFA Women’s World cup.

They used the following thresholds to determine walking,

jogging, running, high speed running and sprinting.

The values in bold feature in

the report and the research here.

We’ve also converted these into other units for you.

Research by Strauss and Lopez Fernandez both use more

bands with smaller increments denoting high speed, running at

4.3 to 5.6 and 4.4 to 5 meters/second and

sprinting as above 5.6 and above 5 meters/second respectively.

If you’re working with junior cohort, the

thresholds may need to be adapted.

Research by Harkness, Armstrong, Till, Datson and Emmons

reduced the number of bands to four.

If you’re wanting to use relative velocity bands in

speed zones, this can either be done by looking

at percentages of max velocity and speed, or by

using percentages of MAS and ASR.

MAS stands for maximum aerobic speed and is

strongly correlated with minimum speed at VO2

max ASR stands for anaerobic speed reserve.

This is estimated using maximum sprint speed.

The benefit of this is to understand

which energy systems the athlete is utilizing

during training sessions and matches.

The paper by Abbott et al. will be linked below

the video to detail how these can be calculated.

Thanks for listening. I hope you found that useful.

Please continue to check in with the

Unleash platform for more content like this.

I’ll see you next time.

Join Catapult’s Kyle Stobbs as he talks through how to use Catapult technologies to mitigate the risk of injury.

____________________________________

Hello everyone, we’ve got a question here.

One of our users asking; how can

I use my devices for injury prevention?

Although we don’t claim to prevent injuries, we

can provide some insight into some ways to

mitigate avoidable injuries such as muscle strains.

So let’s jump right into it.

The big question we get from our users

and how we can do this is by

using the application of loading concepts like this

training stress balance involving the acute chronic workload

ratio and the application of field screening drills.

So in this original concept, Hulin and

colleagues came up with the original training

stress balance concept using cricket fastbowlers.

The concept recommends avoidance of spikes in training

load over an average load between a 7

day period and a 28 day period.

The 7 day period is the acute load.

The 28 day period is the chronic load.

As the ratio of these two variables increase, the

likelihood of injury also increases, which is what we

see in the graph on the right here.

And the acute chronic workload ratio works best on

the variables such as total distance or high speed

distance as a measure of locomotive load, PlayerLoad

as a measure of mechanical load, and heart rate

exertion as a measure of an internal load.

This is a case study showing the soccer

player’s data leading up to a hamstring injury.

Here the user is using PlayerLoad.

Chronic load was displayed on the area line

and acute load is displayed as the bar.

And as you can see there is jumps and

spikes leading up to 1.4, which is out of

the sweet spot shown on the previous slide.

Now for Vector Core Plus and Vector Pro users,

they can utilize the acute chronic widget within OpenField.

Now some considerations if you’re interested in

applying the training stress balance concept, you

need to be mindful of these.

The concerns about the validity of the mathematical

calculation used for the acute period using both

seven day and a 28 day calculations.

If you’re interested in looking more into this, then

I suggest you look at the ‘Un-Coupled’ approach.

You may also be interested in look at the

exponentially weighted model which adapts the original concept by

adding a decaying effect so it places extra emphasis

on the sessions closer to the present day.

Lastly, and most importantly, it doesn’t take into

account individual differences, so it assumes that everyone

reacts the same way to the external stimuli.

So screening drills are the second approach

which I outlined at the beginning.

Using fingerprint drills are a way of testing

our players outside of a lab environment.

To implement these into monitoring practice, you can either

ask a coach to incorporate them into the session

plan or use an existing activity which already takes

place, such as lapse around the pitch.

Ideally, this should be performed at the beginning

of the session to adapt load for individuals

for the remainder of the session if needed.

Once these drills have been performed, you can

tag these in your respective Catapult system.

You will be able to generate norms and

benchmarks for each player to compare against.

System permitting though, try and look at the live

data and promote conversation with players to adapt individual

loading. The reason we use PlayerLoad as

an inferred measure of fatigue is based on the

research of Akenhead and colleagues.

When conducting a repeated sprint protocol, they saw

decreased sprint performance, which is an increase in

sprint time, came with a decrease in player

load caused by decreased vertical stiffness as well

as increased ground contact time.

Such changes can also be observed

in the fingerprint drill data once

normal player load values were established.

If users have access to heart rate data,

they should also look at the internal measure

of load and using heart rate exertion.

This can also be shown as a ratio by

dividing by total distance or player load, higher values will

denote a great internal cost to the athletes per

meter or per player load unit, respectively.

Finally, running symmetry is also a powerful tool for

injury mitigation as well as return to play.

Running symmetry in OpenField is a measure of load

imbalance between left and right leg when running.

However, this is only available for Vector

Core Plus and Vector pro clients.

It’s important to note that players will rarely

have a perfect balance between left and right

foot, so we should look to build norms

for each athlete using a consistent protocol.

Once we have these references, we can look

at differences compared to the norm and

consider standard deviation in a running series.

So thanks for listening.

I hope you found that useful.

Please continue to check in with the

Unleash platform for more content like this.

I’ll see you next time. Bye.