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Opta Pro Forum 2018


I presented a poster back in 2017 at the Opta Pro forum looking at using tracking data to evaluate footballer’s decisions in and around the box, which I've previously written about on Opta Pro’s blog if you want to find out more about the original idea. Opta then kindly invited me back in 2018 to expand on the work and talk about it on the main stage.

Rather than turn up in 2017 with just a poster, I’d created a web app that could animate the tracking data so I could present some real world examples and make it more exciting than just me stood next to a giant piece of paper. I decided to do something similar this time around and do without any slides. Instead, I just talked whilst the web app animated the tracking data Opta had provided. This was rather scary to say the least as you never know when your computer is going to update or crash, or even be compatible with the conference’s audio-visual facilities. Plus, I had no slides to remind me what to say and had to get my timings right so I was talking over the correct section of the match. Thankfully, it all seemed to go smoothly and my computer behaved itself.


Opta have published videos of some of the presentations from that day but as far as I’m aware mine was never released (it could be said have a face for blogs rather than vlogs 🙂) so I’ve added a quick run through of some of the features below. Please note that as part of the agreement to using the tracking data all analyses had to be anonymised, which is why team names and player names are all removed in the video.


After logging in and selecting a match, the first thing I did in the video above was to add a bounding box around each team’s outfield players. This really helps highlight the changes in a team's shape over the course of a game. The demo also calculates the area of the boxes surrounding each team and the ratio between the two so you can quickly see how much of the pitch each team is controlling.

Alternatively, instead of just drawing a box around the players we can be a bit more sophisticated and use a convex hull (at 00:29 in the video). This is the simplest polygon that can be drawn around the players and personally I feel it shows up changes in a team’s shape more clearly than the bounding box, especially if you have a player drifting out of position.

Next up was the central box (at 00:42 seconds into the video), which is an idea that came about when I ran through an early version of my presentation with some of the analysts from Brighton and Hove Albion. They suggested plotting a box between the central midfielders and central defenders to monitor the space between them. Apparently, this is an area of the pitch that Tony Pulis is very keen on so if it's good enough for Tony, it’s good enough for me to include..

Any finally in this section was the Voronoi (at 00:52 seconds into the video). Its become somewhat of a cliché that somebody always presents a Voronoi at the Opta Pro Forum and this year it was my turn. Cliché or not, I really like them and apart from being somewhat hypnotic they are a great way of visualising the space around each player.


I then moved onto talking about some ideas around offensive play, particularly using expected goals (xG). The idea being that if you could track the expected goals for each attacking player’s location on the pitch then you could look at whether players were making the correct decision in who they were choosing to pass to.

The first overlay here at 1:10 in the video shows the expected goals for each attacking player if they were to take a shot from where they were stood based on distance, angle, location and pressure from opposition players near to them. If a player passed to a team mate with a 2% chance of scoring when there’s somebody else with a 10% chance of scoring you could argue that they’ve not made the optimal decision.

This is somewhat simplistic though as it doesn’t account for the difficulty of making that pass in the first place. This is solved by the xgoals (pass) overlay at 1:23 in the video that shows the combined probability of the player with the ball successfully passing to a team mate and that team mate scoring from their current location based on distance, angle, location, opposition pressure etc.

There’s also a line of sight overlay 1:47 in the video that draws a triangle from the player with the ball to the opposition’s goal posts and calculates the number of opposing players inside that triangle blocking the attacker’s view of the goal.


The next section of my presentation moved onto looking at some defensive ideas. The first one was just plotting the offside line to be able to quickly identify players drifting offside. It’s not particular exciting but certainly comes in useful.

What's more interesting though is to plot a line through each team’s defenders (at 2:18 in the video). You can then measure how straight the defensive line is and whether particular players are getting pulled out of position. You can also measure the distance between the defenders and look at how evenly spaced out they, which is the dispersion metric in the video - the higher the dispersion the more uneven the distance between neighbouring defenders is.

Match Summaries

As well as watching the animation to see these metrics changing in realtime, it’s often useful to get a view of how they changed over the full game so I also talked through a few summaries of the data generated by the app (shown at 2:46 in the video).

The first chart shows the area of the home team’s bounding box with blue lines showing when they conceded shots. As you can clearly see they tended to concede shots when their area was the smallest. This is somewhat to be expected as teams naturally compact when they are defending and are pushed back but it would be interesting to analyse the differences between those decreases in area where a shot wasn't conceded with those where it was.

The second chart shows the expected goals for one of the attackers during the match based on his location on the pitch and the defending players around him. During the first half he was constantly in areas where he was threatening the goal, whereas in the second half there is only three occasions where he was potentially in a goal scoring position. As an analyst, you probably wouldn’t want to go and show this chart directly to a coach but it certainly gives you something you should go and analyse the causes of.


Most of the ideas I talked about were intentionally fairly simple as I wanted the presentation to be approachable to the analysts in the audience without getting bogged down in technical explanations. There’s loads of potential to expand on these ideas though. For example I didn’t get time to talk about the distance between the defenders / midfielders or midfielders / attackers and how this correlates with shots. I also skipped over talking about predicting a player’s probability of scoring from a future position rather than their current one so you can analyse the value of players’ runs or through balls etc.

So how was my presentation received? I honestly have no idea as outside a couple of questions from the audience I had no feedback whatsoever. I tried to talk with analysts from a few different clubs after my presentation to get their feedback but was pretty much blanked by all of them. I can only presume teams are either not interested in tracking data, are too scared to talk about it in case they give something away or the ideas I presented just stink.

Personally, I feel there’s a huge amount of insight to be gained from working with tracking data. It’s much more difficult to work with though and it’s much harder to put an engaging presentation / blog together when everything has to be anonymised. Being able to talk about say David Silva’s movement on the pitch would be much more interesting to an audience than talking about Anonymous Player X.

It’s also difficult as an outsider to the football industry as there is no public access to tracking data that I know of. I’m lucky enough to have accumulated a few matches worth of TRACAB data through HackMCFC and the Opta Pro Forum but I still have nowhere near enough to attempt the majority ideas I have. And, even if I did, I’m not sure if I could ever publicly write about it.

Finally, because I want to end on a positive note, I've throughly enjoyed presenting at the Opta Pro Forum each time I’ve been there. It’s always a pleasure to catch up with the guys from Opta and all the bloggers I’ve got to know over the years, and I'm really looking forward to 2019's Forum.

Thanks for reading!


Gianni Pischedda - December 8, 2018

Hi Martin,

great work! Told you so last year at the conference. But then I wondered... how can a coach benefit from it? It is what I think all the time about of any analysis. I think William Spearman (Hudl) presented something similar two years ago, and I was impressed. But later the same thought about practical use came to mind. Also, it is seems to me that to in order to get anything out of it, a coach/analyst would need to watch the whole match or at least the build up of crucial events (shots, goals, etc). I don’t think he has the time. What I think is needed is a software that does all that and then produces (using statistical analysis) a summary of what ‘interesting’ information that it has found by analysing the corresponding data (tracking and event). Also with pointers to the relevant video sequence attached for visual review. Of course, one would need to analyse many matches to find repeating patterns, useful insights. It also would need some input from coaches as to what information would be of help to them

But I do realise that are far too many obstacles for any of us to do that. Aside from getting the data, I am thinking of the computer power needed which I don’t think any of us would have access to.

Wonder if and how William (now at Liverpool) has managed to further develop his system. But I guess now won’t be able to talk about it. Cheers!

BTW Do you know Prozone?

Martin Eastwood - December 8, 2018

Hello Gianni,

Thanks for your message.

Yes, I agree it would be useful to generate insights for coaches from the data rather than expecting them to watch the match back. At the moment there are a number of charts showing how various metrics change over the course of a match that are generated but it would be more useful to automatically flag specific insights for coaches, such as periods of the match where the team shape changes abnormally or the defensive line moves out of position etc. To do this though I need more data to be able to generate the baselines for the metrics to compare against so I can see how / when they move outside the normal range. Sadly, I don’t have access to enough data to do this and there was no interest from anybody at the Opta Forum to take the idea further so it’s not something I would be able to do.

I’ve played around with Prozone a few times in the past but haven’t had chance to dig into it too deeply.



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