The pursuit of wisdom in any walk of life quickly reveals that what you think you know is not nearly enough to get you to where you want to go. As I'm starting out in my football scouting journey I challenged myself to reach out to those already working in various roles in football to answer a short list of questions. My goal wasn't to get answers but relevant perspectives on the game within the game.
Here is Steve Jackson:
Here is Steve Jackson:
How did your first opportunity in football come about?
What attracted you to media/data/scouting? What’s more intriguing now names or numbers?
I’ve always been a football fan and have a maths background – so football data analysis is an obvious interest for me, sitting in the intersection of that Venn diagram. But it’s only relatively recently that I’ve focused on it as a hobby. Discovering the football analytics community on Twitter in 2015 opened up a whole new world to me – I hadn’t realised that there were so many knowledgeable people applying data in smart ways to evaluate players and teams. This spurred me to better understand what could be achieved with football data, and blog about it.
It think it’s the numbers I still find more intriguing. The thing I find so fascinating about football is that so much of what happens in a particular match is random – but over the longer term, outcomes are incredibly consistent. For example, data such as average goals, shots and home advantage tend to be very stable, season after season. But subtle changes can affect that dynamic. Currently we’re seeing examples of this – such as the introduction of VAR.
COVID-19 is another factor. Even though, overall, it could be disastrous for football, some of the changes it’s caused will be fascinating to analyse. We’re already seeing the impact in Germany, with home advantage appearing to diminish with no crowds. The introduction of 5 substitutes is a further change to watch with interest.
Another great thing about football data is that I think we’ve only just touched the surface of what can be achieved with data to understand football, there’s so much more to discover.
What type of player fascinates you? What caught your eye?
I like players that can transcend their team with a moment of individual brilliance – players such as Eric Cantona, Dennis Bergkamp and Steven Gerrard (and obviously Lionel Messi and Cristiano Ronaldo, who do it all the time).
I also like players that maintain consistently high performance over several years. Two Premier League players immediately come to mind. N'Golo Kanté and Riyad Mahrez both impressed in Leicester City’s shock title winning season in 2015/16, and have both gone on to prove they are world class players at other clubs. Mahrez sometimes has limited opportunities at Manchester City, but every time he plays he posts spectacular numbers. My data driven player evaluation has him as as the 3rd best Premier League player in the 2019/20 season.
What is the biggest misconception you’ve found in this space?
That numbers take the fun out of football. I actually think it’s the reverse – and that data analytics can reveal new, innovative ways of playing that make football more exciting. Liverpool are a great example, they’re probably the most advanced English team for data analytics – and over the last few years, the most entertaining. [However, it could be argued that this season they’ve become more efficient and hence less entertaining – which could be numbers driven.]
Another one is expected goals. I think the creation of expected goals, which was one of the concepts that got me interested in football analytics, was a great step forward. And it’s also good that they’re starting to be used by the mainstream media – e.g. on Match of the Day. But xGs are now usually presented as objective figures – like the number of corners or yellow cards. For example, Match of the Day viewers will see xG alongside other match data, presented without explanation that they are just one version of xG based on a particular model’s assumptions and methods. Without context they’re not particularly helpful and could disengage viewers from football data.
If you could start over what skill would you build on first?
Communication – both written and verbal. Clearly, ability to carry out complex data analysis is important, so proficiency in R or Python will be beneficial (although I still often go back to my comfort zone of Excel) – but communicating complex concepts to an (often sceptical) audience, is hard. Good communication skills can also be applied outside of football analytics, and will still be relevant in the future – when the technology to analyse data may have changed.
What is more important domain knowledge or curiosity?
Curiosity, because there’s so much scope for innovation and development in football analytics.
What is your favorite sports moment? Why?
From a football numbers perspective – Leicester winning the Premier League in 2015/16. It challenged what so many people thought they knew about football. It caused modellers (like me) to review their assumptions. What was the real likelihood of Leicester winning the league? (was it 5000-1?). In reality, because we have so little past data, it’s difficult to quantify the actual likelihood.
Leicester also confounded the normal rules about the numbers a team needs to achieve to be successful – by winning the league with the third lowest possession and second lowest pass completion!
What coach or player would you give a lifetime contract to? Why?
Marcelo Bielsa. His transformation of Leeds United over such a short period is remarkable – and it’s amazing to see what his incredible level of professionalism (e.g. spygate and encyclopaedic knowledge of every team) can achieve.
What advice would you give to someone wanting to get into this space?
Identify areas of play or tactics that haven’t been analysed fully yet, but where there’s potential for teams to improve – and focusing on these – is a good way to stand out. @PenaltyKickStat is a good example of this.
Who is your favorite athlete? Why?
Probably Sergio Agüero, who has maintained consistently elite performance over 9 seasons with the same team – and I think is underrated in terms of his contribution to the Premier League.
What is your favorite quote or saying?
"When seagulls follow the trawler it is because they think sardines will be thrown into the sea"
I’ve always been a football fan and have a maths background – so football data analysis is an obvious interest for me, sitting in the intersection of that Venn diagram. But it’s only relatively recently that I’ve focused on it as a hobby. Discovering the football analytics community on Twitter in 2015 opened up a whole new world to me – I hadn’t realised that there were so many knowledgeable people applying data in smart ways to evaluate players and teams. This spurred me to better understand what could be achieved with football data, and blog about it.
It think it’s the numbers I still find more intriguing. The thing I find so fascinating about football is that so much of what happens in a particular match is random – but over the longer term, outcomes are incredibly consistent. For example, data such as average goals, shots and home advantage tend to be very stable, season after season. But subtle changes can affect that dynamic. Currently we’re seeing examples of this – such as the introduction of VAR.
COVID-19 is another factor. Even though, overall, it could be disastrous for football, some of the changes it’s caused will be fascinating to analyse. We’re already seeing the impact in Germany, with home advantage appearing to diminish with no crowds. The introduction of 5 substitutes is a further change to watch with interest.
Another great thing about football data is that I think we’ve only just touched the surface of what can be achieved with data to understand football, there’s so much more to discover.
What type of player fascinates you? What caught your eye?
I like players that can transcend their team with a moment of individual brilliance – players such as Eric Cantona, Dennis Bergkamp and Steven Gerrard (and obviously Lionel Messi and Cristiano Ronaldo, who do it all the time).
I also like players that maintain consistently high performance over several years. Two Premier League players immediately come to mind. N'Golo Kanté and Riyad Mahrez both impressed in Leicester City’s shock title winning season in 2015/16, and have both gone on to prove they are world class players at other clubs. Mahrez sometimes has limited opportunities at Manchester City, but every time he plays he posts spectacular numbers. My data driven player evaluation has him as as the 3rd best Premier League player in the 2019/20 season.
What is the biggest misconception you’ve found in this space?
That numbers take the fun out of football. I actually think it’s the reverse – and that data analytics can reveal new, innovative ways of playing that make football more exciting. Liverpool are a great example, they’re probably the most advanced English team for data analytics – and over the last few years, the most entertaining. [However, it could be argued that this season they’ve become more efficient and hence less entertaining – which could be numbers driven.]
Another one is expected goals. I think the creation of expected goals, which was one of the concepts that got me interested in football analytics, was a great step forward. And it’s also good that they’re starting to be used by the mainstream media – e.g. on Match of the Day. But xGs are now usually presented as objective figures – like the number of corners or yellow cards. For example, Match of the Day viewers will see xG alongside other match data, presented without explanation that they are just one version of xG based on a particular model’s assumptions and methods. Without context they’re not particularly helpful and could disengage viewers from football data.
If you could start over what skill would you build on first?
Communication – both written and verbal. Clearly, ability to carry out complex data analysis is important, so proficiency in R or Python will be beneficial (although I still often go back to my comfort zone of Excel) – but communicating complex concepts to an (often sceptical) audience, is hard. Good communication skills can also be applied outside of football analytics, and will still be relevant in the future – when the technology to analyse data may have changed.
What is more important domain knowledge or curiosity?
Curiosity, because there’s so much scope for innovation and development in football analytics.
What is your favorite sports moment? Why?
From a football numbers perspective – Leicester winning the Premier League in 2015/16. It challenged what so many people thought they knew about football. It caused modellers (like me) to review their assumptions. What was the real likelihood of Leicester winning the league? (was it 5000-1?). In reality, because we have so little past data, it’s difficult to quantify the actual likelihood.
Leicester also confounded the normal rules about the numbers a team needs to achieve to be successful – by winning the league with the third lowest possession and second lowest pass completion!
What coach or player would you give a lifetime contract to? Why?
Marcelo Bielsa. His transformation of Leeds United over such a short period is remarkable – and it’s amazing to see what his incredible level of professionalism (e.g. spygate and encyclopaedic knowledge of every team) can achieve.
What advice would you give to someone wanting to get into this space?
Identify areas of play or tactics that haven’t been analysed fully yet, but where there’s potential for teams to improve – and focusing on these – is a good way to stand out. @PenaltyKickStat is a good example of this.
Who is your favorite athlete? Why?
Probably Sergio Agüero, who has maintained consistently elite performance over 9 seasons with the same team – and I think is underrated in terms of his contribution to the Premier League.
What is your favorite quote or saying?
"When seagulls follow the trawler it is because they think sardines will be thrown into the sea"
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