You've spent a small fortune on the squad, got state-of-the-art facilities and employ a dietician and a psychologist - but today you'll win nothing without an analytics team to crunch the numbers from every aspect of your players' performances. We do the maths so you don't have to...
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00:00Clubs at every level of the Football Pyramid are becoming smarter and more efficient.
00:13How? The use of data.
00:16To hell with conventional wisdom. The way we've been doing it, it's not been working.
00:21Analysts are now recording data from thousands of actions during games and training sessions
00:26to help shape pre-match preparation and post-game debriefs, pinpoint transfer targets and develop young talent.
00:33The genie is out of the bottle, I don't think it's going back in.
00:35We may know more about the opposition than they actually know about themselves.
00:39The growing use of analytics in football has attracted criticism and cynicism.
00:44These are athletes, they're not spreadsheets.
00:48Battle lines have been drawn between the analysts and the traditionalists.
00:52Can football be translated into numbers by data bots?
00:56Or does it require special insight from real football men?
00:59In 2002, one of the most unfashionable teams in Major League Baseball, the Auckland Athletics,
01:17defined the odds to go on a record-breaking 20-game winning streak.
01:22Their success was powered by a new approach to player recruitment, Sabermetrics.
01:28Well it started, I played for 10 years professionally and so when I stopped playing I entered the front office
01:34and I started reading this stuff, again the baseball academics, and it made sense to me.
01:38And I had my own experience with which to look at both sides.
01:41I came from a traditional baseball background as a player and now I was reading this new stuff
01:46that sort of put player performance in order for me.
01:50It was very rational.
01:52I could see why a baseball team was good.
01:55You could look at numbers and explain why they were good instead of sort of looking at things anecdotally
01:59and trying to use non-quantifiable reasons to apply success.
02:06We were one of the smallest teams in the league.
02:08We were actually losing money.
02:10But it also created a great platform.
02:13It meant that if we just did things the same way the New York Yankees,
02:17AKA the Manchester United did,
02:19we were destined to finish where our player wages said we should.
02:22If you had the lowest payroll, you were probably going to finish and last.
02:26So we had the opportunity, because we had nothing to lose, to implement something differently.
02:31The success of the Auckland days encouraged sports teams around the world
02:35to replicate the model pioneered by Billy Bean.
02:38Early adopters believed the Moneyball approach could give them an advantage over their competitors.
02:43We knew it worked on individual players and we were able to apply it to the whole team.
02:48We won four division titles, three division titles and a wildcard.
02:52We averaged almost close to 96, 97 wins per year.
02:57And so we had immediate success.
02:59But the biggest thing, the most important thing is we could at least,
03:02we understood why we were successful and we understood where we went wrong.
03:07I mean the numbers would show us.
03:09Billy Bean had the huge luxury of not looking at relegation.
03:12If you don't have to look at relegation, you can try all kinds of stuff.
03:16Analytics and big data are driving the strategies of major corporations around the world.
03:21And these methods are now filtering into football, from the boardroom to the bootroom.
03:26Football clubs over the last 10-15 years have had to deal with a technological revolution.
03:32What that's meant is they've now started to collect through third-party vendors
03:37lots and lots of data on football.
03:39And that those data primarily originally were collected for fans and for media outlets to use.
03:44They've made their way into the clubs themselves.
03:47And now you have football departments that have to contend with what's kind of an avalanche of information.
03:53Sports data is basically a reconstruction of the match.
03:56Okay, so why do we collect data?
03:58It's basically so we can tell a story of how the match is played.
04:01And so you can look through it in various lenses.
04:04So you could have just event data and how many passes and shots.
04:07But as we know, in football, it's not a great reconstruction.
04:10But if we have the tracking data, so if you can see the dots run around,
04:14we can basically reconstruct the game in a better way.
04:17It's like having a scout at every game.
04:21And not just having a scout at every game, because we're collecting data on everything that the player is doing on the field.
04:27It's like having a scout for every player in every game.
04:31Because everything they do is recorded.
04:33Now it's not so much about collecting the data.
04:36It's making sense of that data.
04:38The stakes are high at the top of the footballing pyramid.
04:41But lower down, one bad season can have catastrophic repercussions.
04:45Small clubs with limited budgets can't afford to make a mistake.
04:49To reduce the risk of acquiring a dud signing,
04:52they're turning to Bean's sophisticated sabermetric approach.
04:56So I like to try and get to the training ground as often as I can
04:59and help out with the guys down there.
05:01But a lot of the time, I'm based here in Ecotristi.
05:03With a full screen set up, I'm surrounded by a lot of energy traders.
05:07And at times, there's million of pounds deals getting made.
05:10And I'm sat here watching League 2 Football and providing analysis.
05:13So it's a pretty unusual workplace.
05:15And probably quite different to a lot of analysts in the Football League.
05:18But it's good.
05:19So here at Ecotristi, I'm the Chief Operating Officer.
05:22In energy trading, we buy and sell energy, mostly buy,
05:25to meet the needs of our customers on a day-to-day basis.
05:28We're able to take a lot of the skills and the data analysis that we undertake
05:33in the trading front of energy into the world of football.
05:37So we saw it as an opportunity to be creative in the data and analytical space.
05:43And see if we can form a competitive advantage at a lower level.
05:48It wasn't really necessarily about budgets.
05:50But it was about trying to maximize what we can get out of every single player that we recruit.
05:56Trying to bring together a list of players that is the best from the manager's eye and augmenting that with the data.
06:03And also performance-wise, we just wanted to understand all aspects of our performance.
06:08So essentially doing the same thing in business and taking that into the world of football.
06:13The Billy Beans story is really originally a story about player recruitment and finding inefficiencies in the market
06:20on the back of going against conventional wisdom, really.
06:24They used data to try and scout players, try to find players that no one else wanted,
06:30that were able to do things that would help the team win.
06:34Manchester United and Burnley are very different clubs, despite the fact that they play in the same league.
06:38And as a result, Burnley has to take a very different approach to putting together a team
06:42than Manchester United.
06:44There's a lot of money being spent, but for more of the mid-level clubs, you know,
06:48there should be bargains available.
06:50So if they're smart with the data, and if they look through it with a certain lens,
06:54they could be able to find some gems out there.
06:59Yeah, this is where all your goals have come from.
07:02So, a lot of them are in a six-yard box.
07:04If we get them both to you in there, that's where you're down to us.
07:06That's my bread and butter, yeah.
07:08The recruitment side for a small club, like you say, is really, really key.
07:11And it's important that we're different.
07:13In January, every club will be after the same players.
07:16And probably we can't compete for those players that everyone's after.
07:20So we have to find other types of players.
07:22And we have a different way of playing.
07:24And we have to find players that can fit into that.
07:26And we have to use the data for that.
07:28Yeah, I think the one I would definitely pick out is Christian Deutsch.
07:30You know, he's been our top scorer last year.
07:33He's our top scorer this year.
07:35I think he's second or third highest goal scorer in the top six English leagues in 2017.
07:42Yeah, I think Christian's done better than we have envisaged.
07:47But we knew that the basics were there.
07:49We knew he could score goals.
07:51We knew he got in the right positions on the pitch because his data showed that.
07:55And it was then a case of us trying to work with him how to convert those chances from the positions he got into,
08:02which his data showed.
08:03So that one is proof of the data works.
08:06I mean, the value for money on that one, to say we paid £30,000 for him,
08:10and he's worth an awful lot more than that now.
08:13Tom, who looks after the data, you know, I'll give him a list of targets.
08:18And he'll go through them and give us graphs in terms of their, you know, value and what they're good at,
08:23what they're not good at, what their metrics are in terms of if it's a striker, goals, expected goals.
08:29As analytics evolves, new metrics arrive, and some are more widely accepted than others.
08:34Expected goals is one example of such a seemingly divisive tool.
08:38So what exactly does it mean?
08:41It's a measuring tool of the probability of that shot from that specific location and resulting in a goal.
08:47So we look at thousands of different shots that occurred in League One, League Two and National League,
08:52so we make it relevant to our level of football.
08:54We'll then apply where it was on the pitch, the angle, the distance.
08:59Was it a headed shot? Was it a shot with the feet? How was it assisted?
09:03Put all those things into an algorithm.
09:06That will then produce a number which will tell us how likely that is to result in a goal.
09:09If the expected goal is 0.15, 15% of the time, a shot from that location will result in a goal.
09:16Well, it makes me feel a lot better about myself because my expected goals is a lot less than what I'm achieving at the moment.
09:23So that's good for me.
09:25Well, I just think football's changing and you've got any little inch you can get, it helps out massively.
09:32And it might be the difference at the end of the season between getting promoted or relegated.
09:37I had nine games without a goal this season and the manager pulled me and said, listen, he said, I don't want you to go.
09:44I know we're having bad results at the moment, but I don't want you to try and get involved and do stuff which you're not as good as.
09:50He said, you're best when you're in the box and you stay the whip for the goals. That's where you score your goals.
09:55And I've done that and I've gone on a little bit of a goal scoring run. So that's where the stats have helped me and the manager.
10:01So it tells me where to run and what positions I should get myself into to help my game as much as possible on the team.
10:07Competing against the Premier League's mega rich requires creative thinking.
10:13To punch above their economic weight, Southampton created the Black Box, a live database collecting player metrics from every major league.
10:21This has enabled them to acquire players of undervalued talent and sell them on for a profit.
10:26Sadio Mane, Dejan Lovren, Morgan Schneiderlin, Victor Wanyama, the list goes on.
10:32A lot of the KPIs that we look for the different positions is something else that's been consistent for quite a while.
10:39So a lot of the scouts know the type of players that we're looking for at the football club.
10:44So they'll already be creating scout reports for any players that they've seen up there.
10:48So they can recommend them to put on our target list and someone that we need to look at as a potential signing for the football club.
10:55But we'll also kind of use the data on a global scale to highlight any top performers.
11:01And from that will be an area that we need to provide some more scouting information on.
11:07So that will be from the eye, from our scouts.
11:10Yes, there are some players that will have been signed because their stats look good.
11:14Payet at West Ham is a good example. Gabriel at Arsenal was a good example of that kind of an approach.
11:20But that's really kind of missing the point. The point of analytics is doing things differently.
11:25One of the reasons for these crazy prices that we're paying for players these days is that people get really wedded to one player.
11:32They really get, they think that this is the guy, we need to have him and we're willing to pay over the odds.
11:37What data can help you do is generate options.
11:41Maybe find guys that are kind of like that other guy or maybe who would fit into the team in a slightly different way.
11:47And it allows you to walk away from a bad deal. It allows you to walk away from a really expensive deal.
11:53Football has actually been collecting the most data for the longest time.
11:57But football is the most complex sport. So it's low scoring, it's continuous, it's time varying.
12:04It's very strategic. It's very subjective. So just say you and I were analysing the game, we could come up with different opinions.
12:11When you compare it to other sports like basketball, it's high scoring. Tennis and American football, they're segmented.
12:18Baseball, it's segmented. You know, it's very easy to do the analysis. You have a lot of data points.
12:23So the key for football is actually to come up with the right language and ask the right question for specific things.
12:31How was our formation? How did we press? How were we on set pieces? Did we attack by the counter attack?
12:39All these different things we have to learn directly from data.
12:44You know, when I played it was a video recorder. And looking at the game back, now we monitor them, you know, every day.
12:52And in terms of their sleep, their training, everything they do really is massive.
12:59We may know more about the opposition than they actually know about themselves.
13:02Yeah, I think a coach's eye can see a certain amount. What the data does is just back that up.
13:08We can look at data of the team we're about to play and we can break down strengths and weaknesses of the team that we're playing.
13:15There was a game a few weeks ago, a game that we actually went on to win. In my opponent report, I noticed that the team played pretty deep.
13:22Their average position was quite deep. And their pressing metrics weren't very high. So they allowed you a lot of time on the ball.
13:27I suggested that we'd be able to play a lot of football. And we did. We surpassed them to death, really.
13:32And then I'd also highlighted in the area where they were weak and they conceded a lot of shots.
13:36I said, if we can get our key players in these areas, there's a fair chance we can score from here.
13:40And we actually scored our first goal in exactly that area.
13:45Data in terms of pre-match, a lot of it is video based. But in terms of statistical data, it's used, I guess, to look at trends.
13:54So it won't be just from one game. We'll look from game to game and build up a database to kind of create a performance profile on that team
14:02and look at any individuals that are maybe performing to a higher level.
14:09The Black Box also helps Southampton develop home grown talent. They can sell for huge profits.
14:14Data helps to drive player recruitment at academy level and to maximise the potential of their scholars.
14:20I started trying when I was eight and then finally signed at nine. So I was quite a young age.
14:26I think when we first got here, it was just a load of numbers on a sheet.
14:29But now we understand what it actually is, the details of it and where we can improve and what we need to look at.
14:35So it's helped me massively. I think when I first got here, I didn't really know what to do.
14:39It was sort of just watching the game. I wasn't really taking notice.
14:43But as I started to learn more, I think I focused on myself more and the positioning I'm taking up
14:48and all the little details you can sort of figure out what you have to do to be better.
14:53And so it's helped me massively develop.
14:55A founding principle of this organisation is youth development. It's everything we stand for.
15:05Excellent. It's potential. It's a strapline. It's everything we work towards.
15:08And even when you buy a senior player, first team player, still the principle is the same.
15:12Can we improve him? Because we may be selling him. And if we are selling him, we need to be selling him for a profit.
15:18So it's all about improving that individual.
15:21It was never really the dream to produce a player to sell. It became the business model when first teams started sliding through the league
15:31and then ultimately into administration. It was selling of players, Theo Walcott's and Alex Roxo Chamberlain's and Gareth Bale.
15:39So we all, as fans and also as a staff member here, we all dream of what happens if we kept hold of those players? What would he have done?
15:46But the reality, if we kept hold of those players, we would have gone out of business.
15:50There's a huge amount of data that's collected around the players from match day data to the way they sleep, to the way they're feeling in the morning,
15:59to training their power outputs in the gym.
16:02The challenge is what do we do with that data and how important is it the analytics around that data?
16:09So on a daily basis, we collect information from players from GPS units.
16:13So we would look at distances covered, the speeds at which they're covered and other information such as accelerations and decelerations.
16:21And we would use that in a more individualised approach so we can optimally adjust their training programmes to make sure that they're fresh and they're in peak condition come match day.
16:34We're now in an amazing position where for the first time we're able to turn down those opportunities to sell players and push back against the big clubs and turn around and say no, not for sale.
16:46Yeah, it's a huge point of the game now. I mean, obviously there's a lot of other sports that use data or heavy analytics.
16:54Soccer has not yet cracked, I don't think, the code yet in terms of what are the key indicators of what's going to make a player successful or not.
17:02I think there's several companies out there that aggregate the data and try to make it easier for you to make a decision.
17:09But at the end of the day, I think soccer people want to still see the player and see how that marries up with the data that you're seeing.
17:17Because sometimes the data doesn't always match what you're seeing on the field because of the free-flowingness of the game and the fluidity of the sport.
17:25Yeah, I think the mentality of a player, I think that sometimes the soccer IQ, and you're only going to get that from seeing sometimes live, obviously video as well,
17:35but also sitting down with that player and having a conversation with them about the game itself, about his particular skill set, about your own club's philosophy on the game,
17:44and see if there's a match there. And you can't get answers from that with data.
17:51Analytics has come a long way from past completion rates and heat maps.
17:56Some of the brightest minds in the game want to find an algorithm to calculate the most valuable intangibles, like team chemistry.
18:04What will this mean for the future of football?
18:07You know, all goals aren't created equal, and the ability to weight the difficulty of those goals,
18:12the player with the skill set to do those things should be rewarded as opposed to a guy who maybe just tapped one in,
18:19because Suarez drew three defenders on him penetrating and he flipped it off to him,
18:24and the other guy just taps it in. Well, the goal gets paid for in today's world.
18:28But shouldn't the guy who created all those things and measuring those things is really the challenge?
18:32And giving proper credit to player performance is what we're all trying to achieve, not just in baseball, but in every sport, just like in business.
18:38So there's lots of cool stuff that people haven't thought about.
18:44So the idea of ghosting, be able to simulate plays that you haven't seen before.
18:50So you can have an example of a play and you can say, well, how does this team defend in that situation?
18:55What happens if I switch that player with another player? How does the outcome change?
18:59In terms of just body shape, okay, where's the player facing? Are they making right decisions?
19:05In terms of injury analytics, player load, fatigue, how's their technique changing over time?
19:10Now, using deep neural networks, we can actually simulate these things.
19:14I think in terms of injury prediction, I think you'll find there'll be less injuries.
19:19So there'll be less soft tissue injuries. You're still going to have the edge cases, but soft tissue injuries, I think they'll be minimised.
19:27I think in terms of player valuation, in terms of performance, I think that'll be normalised.
19:32I think you see the volatility now, it's because we haven't got these good metrics.
19:37However, what you don't take into consideration is the media, okay, the meeting, the shirt sales.
19:42There's all these other things that need to be taken into account.
19:50You're never, I guess, going to have data just making a sole decision, I think, in anything.
19:54But as data advances and the individuals that are part of that process and are creating and maximising the use of data in clubs and in different sports,
20:04I think those people are more crucial in the process and I think data becomes more important in what we do from day to day.
20:12We have to communicate with diamond experts and if we can't speak their language then we're basically not going to be let in.
20:22It's an exciting area to be in because it's constantly evolving and improving. Technology improves.
20:27The genie is out of the bottle, I don't think it's going back in.
20:30When you've got open-minded people, it works really well.
20:34Hopefully you can tell us if you're going to win or lose.
20:36It'll be nice, won't it? If the data can tell me we're going to get three points on a Saturday, it'll save me an awful lot of work.