Month: February 2014

I get a lot of requests for radars of specific players and or years. Apparently the radars are popular, which is great, but I don’t actually have enough time to keep up. This one, however, I have wanted to do for a long time, so here we go.

If this were an art installation, it would be a painful, heart-wrenching piece called “What Have You Done To My Kagawa?!?”

If it were a parody twitter account, it would be vaguely funny, but as racist a portrayal of Asian stereotypes as Mickey Rooney in Breakfast at Tiffany’s. (Seriously, don’t follow EvilKagawa.)

My short answer on why Kagawa looks so much worse than he did at Dortmund is: User Error.

Moyes doesn’t know how to deploy him to get the best out of him, and make no mistake, Kagawa’s best is very very good. He’s one of the best pressing attackers you will find, and he’s one of those guys that creates with his dribbling, passing, and goals. Those things are incredibly valuable, and Moyes has no idea what to do with Japan's best player .

This is one area where I end up having a ton of sympathy for football players. First, you move to a huge club with a genius manager, in the most popular football league in the world. Exciting, right?

That genius manager uses you a bit differently than you are used to, but your stats still look okay. Then said manager retires and the new guy, who clearly is not as good as the old guy, thinks you’re basically useless. This is despite the fact that your two most recent bosses - who were unequivocally brilliant - found your work tremendously valuable.

If this was a normal job, you just leave. Since it’s football and you have a contract, you’re stuck until your agent can negotiate an escape, but in the meantime this new donkey is fucking up the program. He’s screwing up your career path, your chance to make the World Cup team, and generally making your life miserable. Sure, you’re still getting paid well, but there is an awful lot more to life and happiness than getting paid.

I hope he moves this summer. Kagawa is an outstanding player. He deserves a manager who understands his game well enough to let the rest of the world see that’s true.

1. I'm not really sure whether it is suitable for this mailbag column, but I was wondering what you think of the Europa League in general, because I never ever hear you about it.

2. What should Chelsea do with Thibaut Courtois? He probably is one of the best goalkeepers in the world at the moment, but if you take him back at Chelsea you have to ditch Cech or will he be loaned to Atletico for another season?

--Simon

To answer your first question, I’m off on Thursdays, and use that day to take a break from football and pretend I have some sort of social life. Even if Arsenal were playing in the EL, I would not end up watching it, so that’s why I also never talk about it.

The question about Courtois is interesting, especially since I get the feeling he’s ready to jump ship if that situation doesn’t get resolved soon. For those who don’t follow much football outside of England, Courtois is definitely one of the top 5 goalkeepers in Europe right now, despite only being 21. Chelsea own him, but he’s been on loan at Atletico Madrid the last two seasons, and has been a crucial part of their resurgence under Simeone.

On the other side of this issue is Petr Cech, Chelsea’s stalwart goalie who himself is only 31 (middle aged for a GK), and who hasn’t exactly been a slouch. He’s probably capable of another five seasons of top play.

Objectively, it’s a great problem for a team to have, but neither guy is willing to play second fiddle. The question then becomes fairly simple. Which guy is the best player? I personally would go with Courtois. He’s young, but has proven himself at the Champions League level, and is likely to be around for a very long time. That said, Mourinho is often loyal to his best players, and he and Cech have a long history of good results together.

If I were trying to make this decision, I’d have a public skills contest broadcast on Sky like every night for a week. You’d record the footage of the battle/practice matches, and then you’d also have some goalkeeping greats commenting over the top of the footage and grading what they see and explaining why it’s important.

Screw Being Liverpool - tell me you wouldn’t watch this!

The only issue for Chelsea is that if they do choose to bring Courtois home, they will have to find a buyer for Cech if he doesn’t want to play backup. Again, a good problem to have, but not one without potential pitfalls.

Is there a good way to statistically measure the effect of being in a good/bad team on a player's performance?  For example, Bale's numbers at RM this season are outstanding, how much of this improvement is due to him getting better vs being surrounded by better players?  I get the impression that it's generally accepted that say, 10g/10a at a team that gets relegated is a lot more impressive than at a team that wins the title, but has anyone attempted to quantify HOW much more impressive it is?

- Martin (@Mush_27)

Excellent question, and the answer is that work here is ongoing. The top performers every season in the NPG90 metric also tend to come from the very top teams in Europe, of which there actually are not that many. There’s a hefty selection bias involved (the best players tend to move to the teams with the most money), but enough footballers in Europe that you would expect at least some unusual names to crop up each year.

Gareth Bale is an excellent test case, because instead of being surrounded by a bunch of slightly above average players like he was at Spurs, he’s now surrounded by the world’s best and the shooting rates for his teams are very similar. It’s hard to design a better real world experiment than this.

Here is Bale’s radar last season at Spurs.

And here is this year’s Madrid radar.

Now obviously, the analysis isn’t that straightforward. For one, Bale has only played as a forward for the last three seasons of his career – he was originally a left back (and a very good one). Also, Bale is now 24, which means he’s just entering his prime as an attacker – we’d expect him to improve this year and maybe next as well.

That said, this is one helluva leap in production, fuelled largely by the boost in conversion rates for himself and his teammates, which is likely the result of playing on a better team. His key passes per game actually went down, but he's currently breaking the boundary in assists per90 at .68. Helloooooo differences in defensive pressure.

Your question about good performers on smaller teams is tougher to answer. Sometimes you see standout performers on those teams because of talent, and other times you get it because they seem to monopolize the ball (see also: Diamanti). This is one reason why it would be beneficial for us to develop some sort of usage rate in football, and use it to normalize offensive output for comparison.

I also think people like Daniel Altman are looking at the performance value versus overall team quality question from a number of different angles, and will likely publish more on it in the near future. Baby steps.

Some questions about de Eredivisie; what does the model thins about Ajax now. still doesn't like em? They have been not good after the winter break imo, but they did take most of the points.

And what about the relegation race, so many teams still not safe. I know your model really hated RKC all season, but they keep picking up points here and there.. Any thoughts now?

Cheers,

John van Herk

(Note: This question was asked a couple of weeks ago)

Ajax are pretty interesting because much like Liverpool in the Premier League, they have progressed well all season. The model currently has them as the second best team in the Eredivisie behind Twente, but because Twente keep dropping points, Ajax are now the most likely to win the league (by a small margin, anyway). This is the first season I’ve had access to Eredivisie data, so I’m still learning a lot about how the league operates. More info leads to tweaking leads to better predictions down the road.

Regarding relegation, the model still hates Waalwik and still thinks they are the worst team in the league, though they are not as bad as they were in the first half of the season. (To be fair, it would be difficult for them to stay that bad for a full year.) Roda are also most likely to remain in the bottom three, and my model really dislikes Utrecht, though that seems to be in dispute with opinions from other modelers and Eredivisie fans alike. [I have two beer bets made much earlier in the season that they will finish lower than 12^th.]

I think Nijmegen could still climb to safety, but it kind of depends on them playing defense from time to time, which is practically a unicorn for the bottom half of the Dutch league. 38% shots on target average across the league. What the actual fuck?

My question deals with Arteta.  You mentioned a few weeks ago how he comes up in looking at deep lying playmakers and defensive midfielders.  I'd like you to expound upon that, specifically how he rates defensively.  He seems to get a lot of criticism for that (unfairly in my opinion).

Thanks and keep up the good work, Tripp

Looking at the stats (which probably don’t tell you the whole story, but certainly convey a lot of it), Arteta is excellent defensively. He’s also an excellent passer, both when he was an attacking midfielder earlier in his career, and now when he acts as a destroyer and ball recycler for Arsenal. For the past three seasons, Arteta has probably been the best combination of passer and defensive midfielder in the league. Yes, better than Carrick, and just about every other PL DM I can think of.

He was so good in 2011-12 that when he was injured, Arsenal absolutely could not function without him. Some of that dependence has waned, but he’s still probably their number one option at that position in midfield. The worry I have is that he turns 32 next month, and plays a physical position that often gets injured. Arsenal need to make sure they have reliable cover going forward and an eventual succession plan in the next year or two.

Since 2005, Jose Mourinho coached teams have been making a mockery of football’s current possession based stats – namely, time in possession.

Even the “And Back to the Studio” crowd has sobered up from their collective Barcelona Bender and started to realize that time in possession is a stylistic choice, not necessarily a route to winning games. Unfortunately, this has led to a new, insipid trope of citing possession stats just because they’re there.

The number of possessions stat in basketball has formed the basis for the genuinely useful offensive and defensive efficiency ratings and even the incoming ‘databall’ revolution. Do you think it’s possible to develop a similar stat for football, and if so, how would you go about doing it?

MO, Los Angeles

In addition to the “Mou skew” you currently have Simeone’s Atletico team, and earlier versions of Borussia Dortmund where Klopp’s teams rarely dominated possession, they simply dominated the scoreboard.

Alex Olshansky has probably done the most work on different ways of looking at possession so far, including from this article earlier in the week. Mike Goodman’s work on MiPMoP is also an interesting take on possession value and how teams use it.

In a similar vein, I’ve worked on shot pace a couple of times, helping to quantify how quickly teams move the ball up and down the pitch during their matches. It would likely be preferable to look at final third entries for and against instead of shots, but sadly that data isn’t publicly available at the moment.

So to answer your question, there are a number of people casually working on this, and normalizing possession and attacking rates is fairly important, but there are other issues that are probably more interesting and more valuable at this time. We’ll get there.

And that, my friends, is all I have time for. We now have enough followers that I can gather new questions at least once every month, so I’ll start shooting a mailbag at least that often from here out. If you have questions before I ask, it’s best to just shoot me an email at my twitter account name, Gmail, and I’ll file them away for later use. Twitter seems to be losing hashtags, which means I lose everything that comes at me there immediately.

Cheers,

--TK

What follows is a synopsis of my presentation at the OptaPro Forum which was held in London on Thursday 6th February.  This article was first published on the OptaPro blog.

This analysis was only possible due to the data provided to me by Opta.

Expected Goals

The use of Expected Goals (ExpG or xG) as a metric in football is becoming more widespread.  Even though all current versions if this metric are proprietary and use varying calculation methods, the aim of any Expected Goal measure is simply to assign numerical values to the chances of any given shot being scored.

The ExpG model that I and Constantinos Chappas developed produces a goal probability of approximately 3% for any shot that is struck from a central position outside the penalty area.  Over the past year there has been recognition that shooting from long range is sub-optimal and by doing so a team is merely giving up other, more lucrative attacking options – think Tottenham Hotspur and Andros Townsend.

However, although I will admit that I had jumped to this conclusion in my own mind I was conscious that the alternative options open to the player in possession had never been evaluated before (at least not publically).  This desire to establish baseline conversion rates for the different attacking options available to a player who was 25 – 35 yards from goal formed the basis for my Abstract submission.

Opta very kindly granted me access to the detailed match events for the English Premier League 2011/12 and 2012/13 seasons so that I could undertake this study and present my findings.

Those who are interested in the methodology I used can scroll to the bottom of this article, but for the sanity of any casual readers I will go straight to the findings of this study.

How many times was each option chosen?

Figure 1: Number of Opportunities for each FirstAction

Take ons were attempted much fewer than any of the other possible attacking options.  With the exception of internal passes, all the other FirstActions were attempted between 11% - 18% of the time.  At least part of the reason why there were so many internal passes is that some of the passes that were destined for forward central, wings or the corners would have been intercepted within the rectangle.  As I’m using the end co-ordinates of the pass, and intentions can’t be measured, these passes fall into the internal pass category.  

But how often was a goal scored from each option?

As each possible attacking option has not only a chance of the team in possession scoring, but also the move breaking down and the opposition quickly countering I wanted to look at the net goals scored.  It seemed reasonable to assume that the choice of attacking option would have a bearing on how likely the opposition were to score.

To calculate the net goals scored figure for each option I deducted the number of goals scored by the opposition from the number of goals scored by the original attacking team (both within 30 seconds from FirstAction taking place).

Figure 2: Net Conversion Rates for each FirstAction

Shooting is good?

Much to my amazement, the choice of shooting was actually the joint most efficient attacking option for the player in possession to take.

I had certainly expected that a forward central pass would be one of the more efficient attacking options, but due to the lowly 3% success rate of shooting from this area I had expect shots to appear much further down the table.

Eagle eyed readers will have noticed that the net conversion rate for shots of 3.9% is much higher than the 3% I quoted at the start of my piece.  Was I wrong in my initial understanding?

In my dataset a goal was scored directly from the initial shot in 2.9% of cases, however this was further supplemented by goals being scored from another 1.2% of initial shots due to secondary situations, ie rebounds or players following up.  From this figure of 4.1%, a value of 0.2% was deducted to reflect the amount of times that the opposition scored within 30 seconds of the initial shot.  And so we arrive at a net conversion rate of 3.9%.

Another surprising aspect is that, on average, a team only scored 1 in every 40 times that they had possession of the ball in the area under analysis.  Without having any real knowledge, I had expected the number to be higher, but I guess it shows that our perception and memories can be misleading – hence why we should use data to aid us in our decision making processes.

What is the significance of these findings?

If these results can be taken at face value then no longer can we criticise a player for “having a go” from outside the area.  He’s actually attempting one of the most efficient methods to score from his current location.

The findings are even more important to weaker teams.  It appears that the option where the stronger teams have less of an advantage over the weaker teams is actually the option with the highest expected value (along with the forward central pass).  I say that shooting is the option that should favour weaker teams because those teams are less likely to possess a number of players that can thread a well weighted through ball or play an intricate pass.  They are also likely to struggle to attack in sufficient numbers to create an overlap down one of the wings or to have as many players in support of the ball carrier as the stronger teams will have.

As well as it being logical that weaker teams could benefit more from this knowledge than stronger teams, I was able to demonstrate this by ranking the teams based on average league points per game and splitting the sample into two halves – Top Half and Bottom Half teams.

Figure 3: Net Conversion Rates for Top and Bottom Half Teams

As expected, Top Half teams had a higher conversion rate than their Bottom Half equivalents across all FirstActions.  However, we can see that the drop off between the Top and Bottom Half teams is at its lowest for the Shot option and also that a Shot was actually the most efficient option for Bottom Half teams; whereas Forward Central Passes were the most efficient options for the Top Half teams.

Statistical Rigour

I wanted to satisfy myself that the differences in the conversion rates for shots over the other options (excluding forward central passes) were statistically significant.  I also excluded backward passes from these tests as I don’t think players choose a backwards pass with the expectation that their team will score a goal from it.

The Null Hypothesis used was that there were no differences in net conversion rates between the proportions.

Figure 4: p-values for significance in Net Conversion Rates

It can be seen that the Net Conversion Rates for shots are significantly different than corner passes, internal passes and wing passes.  The only option that didn’t reach the statistically significant threshold was shots compared to take ons, and it is my opinion that with a larger data sample these proportions would also emerge as significantly different.

At this stage we have demonstrated that shots from outside the penalty area are just as efficient as forward central passes, and more efficient than the other possible options.  However, I need to address the fact that there could be bias within the net conversion rates of shots.

Possible Sources of Bias

I am aware of four possible sources of bias that could be at play here which could artificially inflate the conversion rates of shots over other options.

1. Team Quality
2. Score Effects / Game State
3. Lack of Defensive Data
4. Natural Selection

I will briefly discuss each of these in turn and address them where possible.

1 - Team Quality Bias

We have already seen that Bottom Half teams convert shots at a higher rate than other options and that Top Half teams also convert shots at a relatively high rate.  There are statistically no significant differences in how Top and Bottom Half Teams convert shots.

2 - Score Effect Bias

It is accepted that the styles and tactics teams use vary depending on the scoreboard.  A team that is trailing are more likely to attack in numbers and a team that is leading may remain more compact.  It could be possible that shots are being attempted, or converted, at different rates depending on the current score line.

To investigate if this was the case I temporarily removed the Opportunities that occurred when there were two goals or more between the teams.  I then analysed the remaining Opportunities by looking separately at Opportunities which arose when the game was tied and when the game was close (ie tied or just one goal between the two teams):

Figure 5: Net Conversion Rate at Close and Tied Game States

Shots in the entire sample were converted at 3.9%, this is the same conversion rate for Opportunities arising when the game is tied and almost the same for Opportunities occurring when a team is leading by just a single goal.

It appears that shots are converted at broadly similar rates regardless of the current match score, and so there is no bias attributable this source.

3 - Lack of Defensive Data

The Opta dataset is very comprehensive in relation to on the ball events, but unfortunately I was not given any data that could help me ascertain the amount of defensive pressure on each Opportunity.

It could be possible that players shoot from Opportunities which have the greatest chance of their team scoring a goal and they only take other options such as passes to the wings or the corners when a shot is not possible.  Conversely, there will also be occasions when a player could take a shot but opts instead to play a ball for an overlapping runner or attempts to thread a through ball inside the penalty area.

I do not have the data to be able to form an opinion on this either way, but am making the reader aware that this could be a potential source of bias.

4 - Natural Selection

In analysing this dataset I do not have knowledge of the tactics that each team attempted to use on match day or the instructions that were handed down by coaches and managers to the players.  The final potential source of bias identified is the possibility that the only players that attempted to shoot from as a FirstAction were elite shooters (think Gareth Bale last season).

A player that is poor at long range shooting could be instructed not to shoot from an Opportunity or to always seek out the elite shooter.  If this was the case, then the 3.9% Net Conversion Rate for shots that was observed in my dataset wouldn’t be representative of the entire sample of Premier League players.

I would counter that by saying that we know that it’s not just elite players that shoot.  There will have been long range shots taken during the last two Premier League seasons by players who are not skilled in shooting.  So this figure of 3.9% will already be diluted (to some unknown extent) by the conversion rate of non-elite long range shooters.

Even if non-elite shooters are expected to have a conversion rate below the average of 3.9%, the magnitude of the buffer in conversion rates enjoyed by shots over the alternative plays of wing, corner and internal passes and take ons are sufficiently large to suggest that taking a shot may even be the optimum FirstAction for non-elite shooters.

Conclusion

The purpose of this study was to establish benchmark conversion rates for each possible attacking Opportunity given a defined area of the pitch.   I knew that I couldn’t capture all the information that was existent for each individual Opportunity but given the extent of the dataset used in this analysis I assume that I have obtained a representative sample on a macro level.

Given the visibly low conversion rates from long range shots I was surprised at just how efficient (relatively speaking) this option was.  This reinforces the fact that it is not enough to simply know the success rate for any option; we must also be able to reference that against the opportunity cost or success rates of the other possible options.

I am not suggesting that players should shoot on every attack; however I have demonstrated that we should be wary of criticising players for attempting to shoot, especially those in less technically gifted teams.  This study has shown that where players have opted to shoot it was, generally, the most efficient option open to them.

Armed with the information in this study it is no surprise that Tottenham had the highest conversion rate of their Opportunities over the last two seasons.  Gareth Bale would certainly have contributed to the success rate last season, but the North London side converted their Opportunities in both seasons at 3.8% and Bale did not have an exceptional shooting performance during the 2011/12 season.

The logic and methodology used in this study could be carried out on other areas of the pitch and thus benchmark conversion rates could be established as required.

Methodology

I followed the flow of individual match events and created possession chains.  For this analysis I was only interested in possession chains which had an attacking event (ie pass, shot or take on) take place within the boundaries of the red rectangle as displayed in Figure 6.  Where an attacking event did take place within the rectangle I labelled this an “Opportunity” and it forms part of this analysis.

The boundaries of the rectangle in Figure 6 can be described (in Opta parlance) as:

80 ≥ x ≥ 67

65 ≥ y ≥ 35

In plain English, I was concentrating on Opportunities which occurred 23 – 37 yards from goal and in the central third of the pitch.

Over the two Premier League seasons there were almost 24,000 such Opportunities to analyse.

Figure 6: Rectangle showing boundaries for Opportunities

For my analysis I decided to have seven categories of attacking options based on the FirstAction carried out by the player within the rectangle.  These were:

• Internal Pass  (red)
• Corners Pass (yellow)
• Wing Pass (black)
• Forward Central Pass (blue)
• Backwards Pass (orange)
• Shot
• Take on

To aid identification the colours noted above relate to the colours of the zone boundaries shown in Figure 7.

Figure 7: Boundaries of Five Passing Zones

To determine whether a goal was scored from each Opportunity I took the time of the FirstAction and allowed a period of 30 seconds to elapse to see if the attacking team scored a goal.  I decided to use 30 seconds in an attempt to allow fluid passing movements to have a reasonable chance of concluding whilst trying not to contaminate the analysis with events from subsequent movements.

The reason that I chose a time based cut off instead of following the move until the team lost possession is that a clearing header by a defender does not necessarily mean the end of an attacking movement as the ball could drop at the feet of the original attacking team.  Creating logic to determine when possession was really lost is challenging and objective, and so I avoided this method.

Free kicks were excluded from this analysis.

M******** (a.k.a “The Scottish Play”)

“Sometimes what doesn’t happen can be just as important as what does”

(Chris Anderson, Footballers Football Show, 2013)

Last week I had the good fortune of being invited to present at the inaugural OptaPro Analytics Forum. This forum featured many interesting guests from a wide variety of backgrounds, including representatives from professional clubs, getting together to talk about how numbers can be used to enhance our understanding of “the beautiful game”.

When contemplating the event later that evening it dawned on me that there had actually been a rather large elephant in the room . Just like superstitious Shakespearian actors,  even though many interesting topics were covered, not a single speaker or audience member mentioned “the M-word”.

Statistics in sport hit the mainstream in 2011 when Brad Pitt starred as former Oakland Athletics’ general manager Billy Beane in “That Movie”. The film tells the story of how a down-on-its-luck, low budget baseball club achieved unprecedented success through the innovative use of statistics. The club’s success is primarily due to Beane (Pitt) and, in the Hollywood version, his hiring of a young stats whiz kid (Jonah Hill).

Around the same time “M********” was premiering, John W Henry was settling in to his new role as owner of Liverpool Football Club. Henry is also owner of the Boston Red Sox baseball team, had worked with Beane previously, and made no secret of his intention to try to implement a similar approach to building and running a “soccer” club.

One of Henry’s first moves was to appoint Frenchman Damian Comolli as “Director of Football Strategy”. Comolli had spoken before of his admiration for the work of people such as Beane, and was immediately given the remit of managing Liverpool’s recruitment strategy.

In the summer of 2011, Comolli oversaw an unprecedented signing spree:

Liverpool’s signings that season included Andy Carroll (£35m), Charlie Adam (£9m), Stewart Downing (£20m) and Jordan Henderson (£16m).

The 2011/12 season was a disaster. Despite winning the League Cup next to none of the signings paid off. Liverpool finished 8^th in the Premier League and both Comolli and manager, Kenny Dalglish, were sacked soon after. It was not difficult to sense a feeling of satisfaction in many quarters that such a numbers driven “experiment” had failed.

What struck me from several discussions at the OptaPro Forum, was that many people who work in this fledgling analytics community now see it as part of their job to defend statistics in sport and rebuild the appetites of club owners and fans for such work.

Although there have been several positive developments recently – notably Chris Anderson and David Sally publishing The Numbers Game and Sky Sports devoting a whole episode of The Footballer’s Football Show to statistics (3^rd December 2013) – there has been an undeniable sense of a stalling, despondency and collective banging of heads against brick walls.

Chris Anderson is a well-respected and thoughtful academic but it was perhaps unfortunate that his co-panellists on The Footballer’s Football Show were Sam Allardyce and our friend Mr Comolli.  Allardyce speaks often of his admiration for statistics and video analysis, but the consistent lack of aesthetic appeal with which his teams often play is surely part of the reason that the phrase “playing the percentages” comes with such negative connotations.

A recent article for When Saturday Comes in the Guardian caused a stir with a scathing attack on the field, including such lines as “these people don’t deserve football” . Only last night journalist and radio DJ Danny Baker tweeted to his 300,000 followers "Surely now we can finally see 'stats' are train spotting bullshit".

Are Allardyce and Comolli really the best advert for football analytics?

 "M********" is an adaptation of a 2003 book by Michael Lewis which, for me, is far more interesting than the movie. This book was a catalyst for an explosion of similar work in the US, whose major sports all seem more prepared to embrace  such work than soccer. Indeed Henry himself is a confirmed speaker at the forthcoming MIT Sloan Sports Analytics Conference alongside  such high profile figures as current Indianapolis Colts quarterback Andrew Luck .

However, in this country it is the juxtaposition of the sugar-coated Hollywood story and the failings of  Comolli-era Liverpool with which most conversations about statistics in football both begin and end.

It seems the shadow of Charlie Adam looms large in more ways than one.

 One analogy that sprung to my mind was the 2011 UK referendum on a proportional representation voting system (bear with me!). I, and many people I know, were advocates of such a system but I remember being not entirely satisfied with the actual proposal put forward by the Yes campaign. As such, I am sure many either abstained or actually voted to keep the current system. I personally voted Yes, not particularly because I liked the system for which I was voting – I just recognised that if the campaign was defeated this time the issue would likely not make its way back onto the political agenda in my lifetime.

Just because Liverpool was the first club to experiment with analytics and it failed, do we now write off the entire field of work for a generation?

It was therefore with some trepidation that I made my way to Birkbeck University this week – how would the forum play out? What would be the response from the army of mysterious and secretive “performance analysts” that the clubs had (almost) all sent? I personally envisaged one of two responses – either “Stats in football? Nonsense. What can you do for me? Tell me to sign Andy Carroll?” or maybe the polar opposite “Stats, brilliant, yes we have been doing that for years but we can’t possibly tell anyone, our managers would shoot us”.

Fortunately what actually transpired was a series of varied and interesting presentations across a wide variety of topics, with equally stimulating debate both during and after. My personal background is in the betting industry, but there were presentations from people in fields as varied as telecommunications, accountancy and theoretical biology. It was hugely rewarding for “outsiders” such as myself to have the opportunity to share our work.

In addition, much of the work was of a level that it was easy to envisage immediate and tangible benefits for clubs resulting from it.

Of particular interest were two presentations of work that is actually currently ongoing at two of the top clubs in the whole of Europe. Pedro Marques (1^st team analyst at Manchester City) presented some of his and his colleagues work on visualising the nature and frequency of passing networks by forthcoming City opponents. This included not just detail on how they collect and look for patterns in the data, but also actual training ground footage of the coaching staff implementing their findings.

Also, representatives from Spanish telecommunications giant Telefonica presented some of the work they have done for none other than FC Barcelona on “Players’ pre- and post-pass movements” – that is, not only measuring what happens when players have the ball, but actually measuring players’ movements when they do not have the ball.

After the event, it was enlightening to talk with representatives sent from Liverpool FC (who were not, incidentally, part of the Comolli regime) about the challenges they faced in trying to rebuild the reputation of analytics at the club in the wake of its previous experience. I think this remains a challenge for the whole industry. A club requires the full and unstinting support of all stakeholders to have any kind of success with data analysis but the vision really has to come right from the top. They spoke positively of the support they continue to receive from John W Henry and, judging by recent performances at least, it seems that finally Henry’s faith could be on its way to being rewarded.

To see clubs of the stature of Liverpool FC, Manchester City and FC Barcelona put their weight behind data analytics should be a huge incentive for other clubs to make similar investments – and not just financial investments.

Personally, I found the forum immensely energising and I hope to be able to continue to do work in this field in the future.

Just don’t mention the M-word.