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 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 Andrew Puopolo:
Here is Andrew Puopolo:
How did your first opportunity in football come about?
My first opportunity in football came in my first year at Harvard through the Harvard Sports Analysis Collective. One of the clubs members (and one of my best friends) Brendan Kent had done analytics work the previous summer for the Portland Timbers, and through his network he met people at the New York Cosmos who were interested in doing something similar, and hired 2 Harvard students as interns for the summer. 6 people applied, and I was one of the lucky two to get the internship!
What attracted you to scouting/analytics? What’s more intriguing now names or numbers?
I've always been really into math (I was captain of the New York City Math Team in High School) and watched/played sports my whole life. My favorite sport is football, and spent a year in London between HS and Uni, going to many matches (from the Champions League to the Conference), which deepened my passion for football. When I started at Harvard, I joined the Harvard Sports Analysis Collective, which combined these two passions.
Who/what is the first player/concept you "found"? What caught your eye?
I'm not sure what you mean by "found', but I think my first main insight that was helpful in football came when I was working for the Cosmos. They were playing New York City FC in the US Open Cup. For the Cosmos, the US Open Cup was the only competition that mattered, so I spent a long time doing statistical opposition analysis in the buildup. We strongly suspected David Villa wouldn't be starting, so I looked at their backup striker Patrick Mullins.
I "found" that Mullins was almost unstoppable in the air (had a very high aerial duel win rate for strikers) but had a low pass accuracy, and passed this onto the coaching staff. In the match, whenever NYCFC took a goal kick, the Cosmos defenders would allow Mullins to win the header uncontested, but take away all passing options. We won the match 1-0, which was a huge result for the club.
Who/what is the player/concept you "missed" on? What did you learn from it?
I'm sure I've missed a bunch, but since I didn't work in football long enough (only for 10 weeks, and 4 years ago) I don't specifically remember anything I "missed" in the traditional sense of the word (like missing Salah as an amazing player).
If you could start over what skill would you build on first?
If I could start over again, I would focus more time on learning the game through the eyes of a player/coach, and learn their language/what they look for. My training is entirely technical (statistics, mathematics, coding) based, and while I've watched the game as a fan for years, it's not the same. The work of the data analyst/scientist is only effective if the insights/tools are understood and utilized by the coaching and management staff. Thus, understanding the language and needs of the coaching staff is crucial to create a "union" (in the words of Joe Mulberry) between data and traditional methods.
Do you see player development as more of an art or a science?
I haven't thought too much about this as I've never done any work in player development, but it's likely a bit of both. This expands on my answer to the previous question. We shouldn't make decisions 100% on the science (say cut a player from an academy bc he fails one metric at age 15), but the data/science is an effective tool to make better decisions. In addition, like the previous question, a good "union" bridges the gap between these two things, as the science will support and generalize the "art".
What is your favorite sports moment? Why?
My favorite sports moment is the Red Sox winning the World Series in 2004. I was only 8 at the time, and a Red Sox fan growing up in New York City. The Red Sox hadn't won in 86 years, and no team had previously come down from 3 games to 0 to win a baseball series. It was my first moment of pure euphoria. However, Spurs winning a trophy (any trophy) would easily eclipse that.
What coach/player/team inspires you? Why?
As much as I dislike the fact that they win a lot and prevent Spurs from winning trophies, Liverpool. They seem to do everything right, right down to the last detail, and have an effective union between sport and data. Will Spearman is an amazing guy with an even more impressive, and is one of the top minds in the industry, yet is only a small piece of a well oiled machine that should be on the top of English football for years to come.
What advice would you give to someone wanting to get into scouting/analytics?
I would follow Ted Knutson's advice. Learn statistics. Learn how to code. Learn the game (see question 5). Learn Data Visualization. And learn to question yourself. The last three are the most important parts, as they provide the key link between statistics/coding to convincing practitioners that your insights are useful. In addition, you need to think about the potential downfalls of your analysis/model, situations it might have missed, and what the counterpoints might be to people who doubt it.
What is your favorite app/tool to use (for work or fun)?
I love using Python, since it has so many different applications/uses.
What other sport/hobby/discipline do you feel improves your work as an analyst/scout? Why?
My first opportunity in football came in my first year at Harvard through the Harvard Sports Analysis Collective. One of the clubs members (and one of my best friends) Brendan Kent had done analytics work the previous summer for the Portland Timbers, and through his network he met people at the New York Cosmos who were interested in doing something similar, and hired 2 Harvard students as interns for the summer. 6 people applied, and I was one of the lucky two to get the internship!
What attracted you to scouting/analytics? What’s more intriguing now names or numbers?
I've always been really into math (I was captain of the New York City Math Team in High School) and watched/played sports my whole life. My favorite sport is football, and spent a year in London between HS and Uni, going to many matches (from the Champions League to the Conference), which deepened my passion for football. When I started at Harvard, I joined the Harvard Sports Analysis Collective, which combined these two passions.
Who/what is the first player/concept you "found"? What caught your eye?
I'm not sure what you mean by "found', but I think my first main insight that was helpful in football came when I was working for the Cosmos. They were playing New York City FC in the US Open Cup. For the Cosmos, the US Open Cup was the only competition that mattered, so I spent a long time doing statistical opposition analysis in the buildup. We strongly suspected David Villa wouldn't be starting, so I looked at their backup striker Patrick Mullins.
I "found" that Mullins was almost unstoppable in the air (had a very high aerial duel win rate for strikers) but had a low pass accuracy, and passed this onto the coaching staff. In the match, whenever NYCFC took a goal kick, the Cosmos defenders would allow Mullins to win the header uncontested, but take away all passing options. We won the match 1-0, which was a huge result for the club.
Who/what is the player/concept you "missed" on? What did you learn from it?
I'm sure I've missed a bunch, but since I didn't work in football long enough (only for 10 weeks, and 4 years ago) I don't specifically remember anything I "missed" in the traditional sense of the word (like missing Salah as an amazing player).
If you could start over what skill would you build on first?
If I could start over again, I would focus more time on learning the game through the eyes of a player/coach, and learn their language/what they look for. My training is entirely technical (statistics, mathematics, coding) based, and while I've watched the game as a fan for years, it's not the same. The work of the data analyst/scientist is only effective if the insights/tools are understood and utilized by the coaching and management staff. Thus, understanding the language and needs of the coaching staff is crucial to create a "union" (in the words of Joe Mulberry) between data and traditional methods.
Do you see player development as more of an art or a science?
I haven't thought too much about this as I've never done any work in player development, but it's likely a bit of both. This expands on my answer to the previous question. We shouldn't make decisions 100% on the science (say cut a player from an academy bc he fails one metric at age 15), but the data/science is an effective tool to make better decisions. In addition, like the previous question, a good "union" bridges the gap between these two things, as the science will support and generalize the "art".
What is your favorite sports moment? Why?
My favorite sports moment is the Red Sox winning the World Series in 2004. I was only 8 at the time, and a Red Sox fan growing up in New York City. The Red Sox hadn't won in 86 years, and no team had previously come down from 3 games to 0 to win a baseball series. It was my first moment of pure euphoria. However, Spurs winning a trophy (any trophy) would easily eclipse that.
What coach/player/team inspires you? Why?
As much as I dislike the fact that they win a lot and prevent Spurs from winning trophies, Liverpool. They seem to do everything right, right down to the last detail, and have an effective union between sport and data. Will Spearman is an amazing guy with an even more impressive, and is one of the top minds in the industry, yet is only a small piece of a well oiled machine that should be on the top of English football for years to come.
What advice would you give to someone wanting to get into scouting/analytics?
I would follow Ted Knutson's advice. Learn statistics. Learn how to code. Learn the game (see question 5). Learn Data Visualization. And learn to question yourself. The last three are the most important parts, as they provide the key link between statistics/coding to convincing practitioners that your insights are useful. In addition, you need to think about the potential downfalls of your analysis/model, situations it might have missed, and what the counterpoints might be to people who doubt it.
What is your favorite app/tool to use (for work or fun)?
I love using Python, since it has so many different applications/uses.
What other sport/hobby/discipline do you feel improves your work as an analyst/scout? Why?
I think playing the game helps understand how to analyze it. While I don't regularly play football, I play a ton of ice hockey, and as a result when I watch professional matches, I have a much more intricate understanding of the strategies and decision making.
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