The Numbers Game - How Data Is Changing Football I FourFourTwo Documentary
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...
Category
🥇
SportsTranscript
00:00 [Chanting]
00:07 Clubs at every level of the football pyramid are becoming smarter and more efficient.
00:13 How? The use of data.
00:16 To hell with conventional wisdom. The way we've been doing it, it's not been working.
00:21 Analysts are now recording data from thousands of actions, during games and training sessions,
00:26 to help shape pre-match preparation and post-game debriefs,
00:30 pinpoint transfer targets and develop young talent.
00:33 The genie is out of the bottle, I think it's going back in.
00:35 We may know more about the opposition than they actually know about themselves.
00:39 The growing use of analytics in football has attracted criticism and cynicism.
00:44 These are athletes, they're not spreadsheets.
00:47 Battle lines have been drawn between the analysts and the traditionalists.
00:53 Can football be translated into numbers by data bots?
00:56 Or does it require special insight from real football men?
01:00 In 2002, one of the most unfashionable teams in Major League Baseball, the Auckland Athletics,
01:17 defied the odds to go on a record-breaking 20-game winning streak.
01:22 Their success was powered by a new approach to player recruitment, Saber Metrics.
01:28 Well it started, I'd played for 10 years professionally,
01:31 and so when I stopped playing I entered the front office and I started reading this stuff.
01:35 Again, the baseball academics, it made sense to me.
01:38 And I had my own experience with which to look at both sides.
01:41 I came from a traditional baseball background as a player,
01:44 and I was reading this new stuff that sort of put player performance in order for me.
01:49 It was very rational.
01:52 I could see why a baseball team was good.
01:55 You could look at numbers and explain why they were good,
01:57 instead of sort of looking at things anecdotally
01:59 and trying to use non-quantifiable reasons to apply success.
02:05 We were one of the smallest teams in the league, we were actually losing money.
02:10 But it also created a great platform.
02:13 It meant that if we just did things the same way the New York Yankees,
02:17 aka the Manchester United did,
02:19 we were destined to finish where our player wages said we should.
02:23 If you had the lowest payroll, you were probably going to finish in last.
02:27 So we had the opportunity, because we had nothing to lose,
02:30 to implement something differently.
02:32 The success of the Auckland days encouraged sports teams around the world
02:35 to replicate the model pioneered by Billy Bean.
02:38 Early adopters believed the moneyball approach
02:41 could give them an advantage over their competitors.
02:44 We knew it worked on individual players, and we were able to apply it to the whole team.
02:49 We won four division titles, three division titles in a wildcard,
02:53 and averaged almost 96, 97 wins per year.
02:57 So we had immediate success.
03:00 But the biggest thing, the most important thing,
03:02 is we understood why we were successful,
03:05 and we understood where we went wrong.
03:07 I mean, the numbers would show us.
03:09 Billy Bean had the huge luxury of not looking at relegation.
03:13 If you don't have to look at relegation, you can try all kinds of stuff.
03:16 Analytics and big data are driving the strategies of major corporations around the world.
03:21 And these methods are now filtering into football,
03:24 from the boardroom to the boot room.
03:26 Football clubs over the last 10, 15 years
03:29 have had to deal with a technological revolution.
03:32 What that's meant is they've now started to collect,
03:35 through third-party vendors, lots and lots of data on football.
03:39 And those data, primarily, originally were collected for fans and for media outlets to use.
03:44 They've made their way into the clubs themselves,
03:47 and now you have football departments that have to contend
03:50 with what's kind of an avalanche of information.
03:53 Sports data is basically a reconstruction of the match.
03:56 Okay, so why do we collect data?
03:58 It's basically so we can tell a story of how the match is played.
04:01 And so you can look through it in various lenses.
04:04 So you could have just event data, and how many passes and shots.
04:07 But as we know, football, it's not a great reconstruction.
04:10 But if we had the tracking data, so if you can see the dots run around,
04:14 we can basically reconstruct the game in a better way.
04:17 It's like having a scout at every game.
04:22 And not just having a scout at every game,
04:24 because we're collecting data on everything that the player is doing on the field.
04:28 It's like having a scout for every player in every game,
04:31 because everything they do is recorded.
04:33 Now it's not so much about collecting the data,
04:36 it's making sense of that data.
04:38 The stakes are high at the top of the footballing pyramid,
04:41 but lower down, one bad season can have catastrophic repercussions.
04:45 Small clubs with limited budgets can't afford to make a mistake.
04:49 To reduce the risk of acquiring a dud signing,
04:52 they're turning to Bean's sophisticated sabermetric approach.
04:56 So I like to try and get to the training ground as often as I can
04:59 and help out with the guys down there.
05:01 But a lot of the time I'm based here in Eco-Tristy,
05:04 with a full screen set up, I'm surrounded by a lot of energy traders,
05:07 and at times there's a million pounds deals getting made,
05:10 and I'm sat here watching League 2 football and providing analysis,
05:13 so it's a pretty unusual workplace,
05:15 and probably quite different to a lot of analysts in the football league,
05:18 but it's good.
05:19 So here at Eco-Tristy I'm the Chief Operating Officer.
05:22 In energy trading we buy and sell energy, mostly buy,
05:25 to meet the needs of our customers on a day-to-day basis.
05:28 We're able to take a lot of the skills and the data analysis
05:32 that we undertake in the trading front of energy
05:35 into the world of football.
05:37 So we saw it as an opportunity to be creative in the data and analytical space,
05:42 and see if we can form a competitive advantage at a lower level.
05:48 It wasn't really necessarily about budgets,
05:50 but it was about trying to maximise what we can get
05:53 of every single player that we recruit,
05:56 trying to bring together a list of players that is the best
06:00 from the manager's eye, and augmenting that with the data.
06:04 And also performance-wise, we just wanted to understand
06:07 all aspects of our performance.
06:09 So it's essentially doing the same thing in business
06:11 and taking that into the world of football.
06:14 The Billy Bean story is really originally a story about player recruitment
06:18 and finding inefficiencies in the market
06:20 on the back of going against conventional wisdom, really.
06:24 They use data to try and scout players,
06:27 try to find players that no one else wanted,
06:30 that were able to do things that would help the team win.
06:34 Manchester United and Burnley are very different clubs
06:36 despite the fact that they play in the same league.
06:38 And as a result, Burnley has to take a very different approach
06:41 to putting together a team than Manchester United.
06:44 There's a lot of money being spent,
06:46 but for more the mid-level clubs, there should be bargains available.
06:50 So if they're smart with the data,
06:52 and if they look through it with a certain lens,
06:54 they could be able to find some gems out there.
06:57 Yeah, this is where all your goals come from.
07:02 So a lot of them are in the six-yard box.
07:04 We get the ball to you in there, that's where you're dangerous.
07:07 The recruitment side for a small club, like you say, is really, really key.
07:11 And it's important that we're different.
07:13 In January, every club will be after the same players.
07:16 And probably we can't compete for those players that everyone's after,
07:20 so we have to find other types of players,
07:22 and we have a different way of playing.
07:24 And we have to find players that can fit into that,
07:26 and we have to use the data for that.
07:28 Yeah, I think the one I would definitely pick out is Christian Deutch.
07:31 He's been our top scorer last year, he's our top scorer this year.
07:36 I think he's second or third highest goal scorer
07:39 in the top six English leagues in 2017.
07:42 Yeah, I think Christian's done better than we envisaged,
07:47 but we knew that the basics were there.
07:49 We knew he could score goals.
07:51 We knew he got in the right positions on the pitch,
07:54 because his data showed that.
07:55 And it was then a case of us trying to work with him
07:58 how to convert those chances from the positions he got into,
08:02 which his data showed.
08:03 So that one is proof of the data works.
08:06 I mean, the value for money on that one,
08:08 to say we paid £30,000 for him,
08:10 he's worth an awful lot more than that now.
08:13 Tom, who looks after the data, I'll give him a list of targets.
08:18 He'll go through them and give us graphs in terms of their value,
08:22 what they're good at, what they're not good at,
08:24 what their metrics are in terms of if it's a striker, goals, expected goals.
08:28 As analytics evolves, new metrics arrive,
08:31 and some are more widely accepted than others.
08:34 Expected goals is one example of such a seemingly divisive tool.
08:38 So what exactly does it mean?
08:41 It's a measuring tool of the probability of that shot
08:44 from that specific location and resulting in a goal.
08:47 So we look at thousands of different shots
08:50 that occurred in League 1, League 2 and National League,
08:52 so we make it relevant to our level of football.
08:54 We'll then apply where it was on the pitch, the angle, the distance,
08:59 was it a headed shot, was it a shot with the feet, how was it assisted?
09:03 Put all those things into a algorithm that will then produce a number
09:07 which will tell us how likely that is to result in a goal.
09:10 If the expected goal is 0.15, 15% of the time,
09:13 a shot from that location will result in a goal.
09:16 Well, it makes me feel a lot better about myself
09:19 because my expected goals is a lot less than what I'm achieving at the moment,
09:23 so that's good for me.
09:26 I just think football's changing and you've got to...
09:29 Any little inch you can get, it helps out massively,
09:32 and it might be the difference at the end of the season
09:35 between getting promoted or relegated.
09:38 I had nine games without a goal this season,
09:41 and the manager pulled me and said, "Listen, I don't want you to go...
09:45 "I know we're having bad results at the moment,
09:48 "but I don't want you to try and get involved and do stuff which you're not as good at."
09:52 He said, "You're best when you're in the box and you stay the width of the goals.
09:56 "That's where you score your goals."
09:58 I've done that and I've gone on a little bit of a goal-scoring run,
10:01 so that's where the stats have helped me and the manager.
10:04 It tells me where to run and what positions I should get myself into
10:07 to help my game as much as possible and the team.
10:10 Competing against the Premier League's mega-rich requires creative thinking.
10:14 A bunch above their economic weight, Southampton created the Black Box,
10:18 a live database collecting player metrics from every major league.
10:22 This has enabled them to acquire players of undervalued talent
10:25 and sell them on for a profit.
10:27 Sadio Mane, Dejan Lovren, Morgan Schneiderlin, Victor Wan-Yama - the list goes on.
10:33 A lot of the KPIs that we look for the different positions
10:37 is something else that's been consistent for quite a while.
10:40 A lot of the scouts know the type of players that we're looking for at the football club,
10:44 so they'll already be creating scout reports for any players that they've seen up there,
10:49 so they can recommend them to put on our target list
10:51 and someone that we need to look at as a potential sign-in for the football club.
10:55 But we'll also use the data on a global scale to highlight any top performers,
11:01 and from that will be an area that we need to provide some more scouting information on,
11:07 so that will be from the eye, from our scouts.
11:10 Yes, there are some players that will have been signed because their stats look good.
11:14 Payet at West Ham is a good example.
11:16 Gabriel at Arsenal was a good example of that kind of an approach.
11:20 But that's really kind of missing the point.
11:22 The point of analytics is doing things differently.
11:25 One of the reasons for these crazy prices that we're paying for players these days
11:29 is that people get really wedded to one player.
11:32 They really get, they think that this is the guy, we need to have him,
11:36 and we're willing to pay over the odds.
11:38 What data can help you do is generate options.
11:41 Maybe find guys that are kind of like that other guy,
11:44 or maybe who would fit into the team in a slightly different way.
11:48 And it allows you to walk away from a bad deal.
11:50 It allows you to walk away from a really expensive deal.
11:53 Football has actually been collected the most data for the longest time.
11:57 But football is the most complex sport.
12:01 So it's low scoring, it's continuous, it's time varying.
12:04 It's very strategic, okay?
12:06 It's very subjective, so just say you and I were analysing a game.
12:09 We could come up with different opinions.
12:11 When you compare it to other sports like basketball, it's high scoring.
12:15 Tennis and American football, they're segmented.
12:18 Baseball, it's segmented.
12:20 You know, it's very easy to do the analysis.
12:22 You have a lot of data points.
12:23 So the key for football is actually to come up with the right language
12:28 and ask the right question for specific things.
12:31 How was our formation?
12:32 How did we press?
12:34 How were we on set pieces?
12:36 Did we attack by the counter-attack?
12:39 All these different things we have to learn directly from data.
12:42 When I played, it was a video recorder.
12:46 And looking at the game back now, we monitor them every day
12:52 in terms of their sleep, their training, everything they do really.
12:57 It's massive.
12:58 We may know more about the opposition than they actually know about themselves.
13:02 I think as a coach, I can see a certain amount.
13:05 What the data does is just back that up.
13:07 We can look at data of the team we're about to play
13:10 and we can break down strengths and weaknesses of the team that we're playing.
13:15 There was a game a few weeks ago, a game that we actually went on to win.
13:18 In my opponent report, I noticed that the team played pretty deep.
13:22 Their average position was quite deep and their pressing metrics weren't very high.
13:25 So they allowed you a lot of time on the ball.
13:27 I suggested that we'd be able to play a lot of football and we did.
13:30 We passed them to death really.
13:32 I'd also highlighted an area where they were weak and conceded a lot of shots.
13:36 I said if we can get our key players in these areas,
13:38 there's a fair chance we can score from here.
13:40 We actually scored our first goal in exactly that area.
13:43 Data in terms of pre-match, a lot of it is video based.
13:48 But in terms of statistical data, it's used to look at trends.
13:53 So it won't be just from one game, we'll look from game to game
13:57 and build up a database to create a performance profile on that team
14:02 and look at any individuals that are maybe performing to a higher level.
14:06 The black box also helps Southampton develop home-grown talent
14:12 they can sell for huge profit.
14:14 Data helps to drive player recruitment at academy level
14:17 and to maximise the potential of their scholars.
14:20 I started training when I was eight and then finally signed at nine.
14:24 So I'm quite young.
14:27 I think when we first got here it was just a load of numbers on a sheet
14:29 but now we understand what it actually is, the details of it
14:33 and where we can improve and what we need to look at.
14:36 It's helped me massively.
14:37 I think when I first got here I didn't really know what to do.
14:40 Just watching the game I wasn't really taking notice
14:43 but as I started to learn more, I think I focused on myself more
14:47 and the positioning I'm taking up and all the little details
14:50 you can figure out what you have to do to be better.
14:53 So it's helped me massively develop.
14:56 A founding principle of this organisation is youth development.
15:01 It's everything we stand for, excellence, potential, it's a strapline
15:04 it's everything we work towards.
15:06 And even when you buy a senior player, first team player
15:08 still the principle is the same.
15:10 Can we improve him? Because we may be selling him
15:13 and if we are selling him, we need to be selling him for a profit
15:16 so it's all about improving that individual.
15:19 It was never really the dream to produce a young player
15:23 it was never really the dream to produce a player to sell.
15:26 It became the business model when first teams started sliding
15:30 through the leagues and ultimately into administration
15:33 it was selling of players, Theo Walcott and Alex Oxlade-Chamberlain
15:37 and Gareth Bale.
15:39 We all, as fans and also as a staff member here, we all dream
15:43 of what happens if we kept hold of those players, what would he have done?
15:46 But the reality is if we kept hold of those players we would have gone out of business.
15:50 There's a huge amount of data that's collected around the players
15:54 from match day data to the way they sleep, to the way they're feeling
15:58 in the morning, to training their power outputs in the gym.
16:02 The challenge is what do we do with that data and how important
16:06 is it, the analytics around that data.
16:09 So on a daily basis we collect information from players
16:12 from GPS units so we would look at distances covered, the speeds
16:16 at which they're covered and other information such as accelerations
16:20 and decelerations and we would use that in a more individualised approach
16:24 so we can optimally adjust their training programmes to make sure
16:29 that they're fresh and they're in peak condition come match day.
16:34 We're now in an amazing position where for the first time we're able
16:38 to turn down those opportunities to sell players and push back
16:41 against the big clubs and turn around and say no, not for sale.
16:46 It's a huge point of the game now. Obviously there's a lot of other sports
16:52 that use data or heavy analytics. Soccer has not yet cracked,
16:56 I don't think, the code yet in terms of what are the key indicators
17:00 of what's going to make a player successful or not.
17:03 I think there's several companies out there that aggregate the data
17:07 and try to make it easier for you to make a decision.
17:09 But at the end of the day, I think soccer people want to still see
17:12 the player and see how that marries up with the data that you're seeing
17:17 because sometimes the data doesn't always match what you're seeing
17:20 on the field because of the free-flowingness of the game
17:24 and the fluidity of the sport.
17:26 I think the mentality of a player, I think that sometimes the soccer IQ
17:31 and you're only going to get that from seeing sometimes live,
17:34 obviously video as well, but also sitting down with that player
17:37 and having a conversation with them about the game itself,
17:40 about his particular skill set, about your own club's philosophy on the game
17:45 and see if there's a match there.
17:47 And you can't get answers from that with data.
17:51 Analytics has come a long way from past completion rates and heat maps.
17:56 Some of the brightest minds in the game want to find an algorithm
17:59 to calculate the most valuable intangibles, like team chemistry.
18:04 What will this mean for the future of football?
18:07 All goals aren't created equal and the ability to weight the difficulty
18:11 of those goals, the player with the skill set to do those things
18:14 should be rewarded as opposed to a guy who maybe just tapped the one in
18:19 because Suarez drew three defenders on him, penetrating,
18:22 and he flipped it off to him and the other guy just taps it in.
18:25 Well, the goal gets paid for in today's world.
18:28 But shouldn't the guy who created all those things and measuring those things
18:31 is really the challenge? And giving proper credit to player performance
18:34 is what we're all trying to achieve, not just in baseball, but in every sport,
18:37 just like in business.
18:39 So there's lots of cool stuff that people haven't thought about.
18:44 So the idea of ghosting, be able to simulate plays that you haven't seen before.
18:50 So you can have an example of a play and you can say,
18:53 "Well, how does this team defend in that situation?
18:56 What happens if I switch that play with another play?
18:58 How does the outcome change in terms of just body shape?
19:02 Okay, where's the player facing? Are they making the right decisions?
19:05 In terms of injury analytics, player load, fatigue,
19:08 how's their technique changing over time?"
19:10 Now, using deep neural networks, we can actually simulate these things.
19:14 I think in terms of injury prediction, I think you'll find there'll be less injuries.
19:19 So there'll be less soft tissue injuries.
19:21 You're still going to have the edge cases, but soft tissue injuries,
19:25 I think they'll be minimized.
19:28 I think in terms of player valuation, in terms of performance,
19:31 I think that'll be normalized.
19:33 I think you see the volatility now is because we haven't got these good metrics.
19:37 However, what you don't take into consideration is the media.
19:41 The media, the shirt sales, there's all these other things
19:44 that need to be taken into account.
19:46 You're never, I guess, going to have data just making a sole decision, I think, in anything.
19:54 But as data advances and individuals that are part of that process
19:58 and they're creating and maximizing the use of data in clubs and in different sports,
20:04 I think those people are more crucial in the process.
20:08 And I think data becomes more important in what we do from day to day.
20:12 We have to communicate with domain experts.
20:18 And if we can't speak their language, then we're basically not going to be able to identify them.
20:22 It's an exciting area to be in because it's constantly evolving and improving.
20:26 As technology improves.
20:28 The genie is out of the bottle, and I think it's going back down.
20:31 When you've got open-minded people, it works really well.
20:35 Hopefully, it can tell us if we're going to win or lose.
20:37 If the data can tell me we're going to get three points on a Saturday,
20:40 it'd save me an awful lot of work.
20:43 [Music]
20:46 (bubbling)
20:48 [BLANK_AUDIO]