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my statsbomb story

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I’ve told this story so many times that I figured it was best to write it all out to ensure I wouldn’t forget any details.

It’s one of the defining stories of my life, and quite a fun, unique story that showcases the power of the internet (still underrated in 2024), and the crazy luck that can happen when you attempt hard stuff.

It’s a story of how a group of random hobbyists met online, founded separate companies in Egypt and the UK continents apart , and ultimately built one of the world’s leading sports data companies, with 8 figures in recurring revenue and prestigious clubs like Liverpool, AS Roma and more as clients.

Dreaming of Tennis Stardom

When I was 12, my life goal was to be a tennis star. I wasn’t the most gifted athlete, but a combination of living a few steps from some tennis courts, and wanting to be away from home meant I spent enough time on the courts to improve enough where stardom felt plausible.

I got obsessive over improving and frustrated at generic advice from coaches, and started doing unconventional things like videotaping my practice sessions, and carrying around flashcards during matches to calculate my serving percentages during changeovers.

I never got anywhere with my tennis career, but it’s funny to think about how that was the beginning of a lifelong obsession with understanding sports and athletes better.

(P.S. it’s funny how much of life is figuring out what you already knew about yourself!)

College in Boston: Finding Inspiration

Despite it being obvious in hindsight, I went to college with no clue about what I wanted to do. I studied finance because engineering didn’t appeal to me, and spent my college years bouncing around a different hobby/idea every few months.

Being in Boston helped, as the city was sports-obsessed. I was introduced to a more data-friendly sports media (shoutout Zach Lowe) and the MIT Sloan Sports Conference, which was the first time I felt excited about a sector and knew I wanted to work in sports analytics.

Blogging to Building

Inspired by the MIT Sloan Sports Conference, I started writing data-driven analysis for any website that would allow me and launched my own blog, TalataBont. Needing data, I emailed data companies, but their $25K quotes led me to learn coding and databases to scrape data from public websites.

One of my first articles in 2014!

These were fun times that remain 80% of my limited coding knowledge and, most importantly, connected me with like-minded people. One early prediction I’m proud of was calling a Zamalek title six games into the league season:

Zamalek – Potential 2014-15 Egyptian Premier League Champions?

One of the first comments on my first technical article was from James Yorke, who later became one of StatsBomb’s first employees and continues to be a key figure there.

Meeting Hesham and Moving Back to Cairo

Hesham’s twitter DM to me

The history of the company is full of cold DMs on twitter, and this was the first significant one. Hesham was an architect who wanted to work in sports, and had built a small media account focused on data content for the Egyptian league. He had read some of my work and wanted to collaborate.

Hesham is the single biggest reason this company exists. We started working together with a local data provider to build content. One of the league’s best coaches saw some of our work and reached out and we immediately had our first ever professional client, doing some consulting for first division teams.

Our hardest ever (and most impactful) decisions were taken by Hesham:

  • Dropping our relationship with our data provider and starting to collect data ourselves
  • Quitting his full-time job with a toddler and going full-time while we made no money

On both these decisions, I was opposed and scared, despite having fewer rational reasons to be so risk-averse, and it was Hesham’s courage that carried us through!

An interview we sat down for in 2018

Launching ArqamFC

After a year at a venture fund in Dubai, I gained ideas about startups and eventually the courage (and savings) to quit and move back to Cairo to work full-time.

Since we had made the decision to collect our own data, our first order of business was figuring out how to. We started with pen and paper, moved up to pirated and open-source software, and eventually ended up building our very first internal data generation tool to help our analysts watch and record matches. We obsessed over data design and quality, issues we had struggled with when buying data.

Initially, we coded insights and data quality scripts in Excel using VBA. As we progressed, we taught ourselves Python and hired software engineers to automate the data pipeline, producing metrics and quality control feedback. We created reports for coaches using Tableau, exported them to PDFs, but struggled to convince coaches in Egypt to pay for our work.

We tried to pivot to media, building brands that do “data-driven” content using our data, and had slightly more success there, growing our brands into just over a million followers. Despite decent traction and engagement, we struggled to monetize our media success.

We had no real understanding of technology or AI, we just built what we thought was missing, and what we didn’t have money to buy!

A Cold Email That Changed Our Life

A year and a half in, we were running out of money, and ideas to generate revenue. We had pivoted slightly to content and media, and there was momentum but no real revenue growth. We had built this data collection capability, with a lot of thought into data design and quality, but we didn’t really know how to use it or convince people to buy it.

Thankfully, we hadn’t taken on any real money (we had put in a combined $20k of savings and family and friends money), and we could choose to shut down at anytime with minimal fuss.

We had modeled some of our ideas and data concepts around a blog turned consultancy called StatsBomb – and I had cold e-mail the founder asking for advice a year earlier before quitting:

We had hopped on a Skype (!) call and chatted a bit and nothing had come of it. 10 months later, as we were considering shutting down, I got a message back from Ted, the CEO:

Teds twitter DM to me 10 months after I reached out

Halfway across the world in a completely different environment, StatsBomb had gone through a similar story to Arqam. They had started as a blog, turned into a consultancy, bought data from a provider, argued with provider over terms, and eventually realized that there is no real value in the industry unless you own your own data.

We had stumbled into the solution (just collecting data ourselves) by virtue of being cashless and out of options.

StatsBomb Partnership: Asking for Equity

Ted asked for a sample of games. It was about 10 times our capacity, but we said yes and scrambled to meet the target. We hit 50%, but the quality was good. We explained our scaling problems and decided to work together. StatsBomb asked for exclusivity, covering our costs and paying us a 12% royalty on sales.

This was the biggest decision we’ve ever made. We had product-market fit as a data-producing back-office for StatsBomb, and they covered our costs. Despite being young, broke (our salaries were $300/month), and desperate. But we had larger ambitions than being a BPO type data factory, and exclusivity would’ve killed any scale we could’ve accomplished there. So we pushed back and asked for equity:

Our email asking for equity at SB

At some point, we realized a small share swap wouldn’t accomplish much, and we should push for a full merger.

I tried to put my background as a banker to use and come up with a relative valuation that would make sense for two very early start-ups. I have notes from the exercise that are fun to go through now:

“This isn’t a merger this is the founding of a dominant vertically integrated company”

“Our value is what we unlock in terms of vertical integration and cheap data generation”

We tried to use the commercial agreement (12.5% + costs) and some assumptions on what percentage of customers buy just data vs. Value Added Services SB provide to come to a fair valuation.

In August of 2018, ten months after Ted’s cold Twitter DM, StatsBomb proposed a merger where they’d acquire 100% of Arqam for some cash and a significant portion of StatsBomb. Functionally, we had already been working as one company, and we accepted the offer.

Building The Company

We’ve spent a lot of time on the first few years of the company, but all of the actual work and success happened after the acquisition. Prior to the acquisition, Arqam had 2-3 clients and no recurring revenue, and StatsBomb had ~$100k and didn’t own their data.

The next 4-5 years are a blur of learning how to build software, teams and sell. I took every product course I could get my hands on, I sent over 1,000 linkedin cold DMs to try to hire (and got maybe 1 good hire from them).

We made every single mistake in the book, from senior engineers rewriting our entire architecture in new languages then leaving, to poorly thought out incentives for data collectors.

But our customers liked our data, we had room to fail due to the low cost base and we found ourselves (mostly by luck) on the right side of the Computer Vision (CV) and AI revolution that was ongoing.

From one of our investor decks in 2022 – a good summary of what I spent most of time on

The whole Computer Vision angle is worth a separate blog post, but in 2016 when we were starting this company, I thought CV and technology would eventually destroy our data generation value.

I had no idea: how valuable our data was as training data, and how much “AI” startups were secretly depending on human collectors:

Over six years, we grew from 2 customers to 300, and $100k revenue to 8-figures in recurring revenue.

We went from some random social accounts, to a scrappy four person startup in Cairo to 1,000+ people over three continents.

The hardest (and most satisfying) part was always finding the people who believed in what we were doing enough to join. These people trusted us enough to forgo whatever career path they were on and build together and for that I am forever grateful.

A special mention to my brother Saad Shahd – who has taught me most of what I know about engineering, building tech products, and becoming a better manager. Late nights in the office building with Saad and talking about everything from politics to relationships will always be one of my favorite memories.

A Bittersweet Ending

Despite success we wouldn’t have dreamed of when starting, this story wouldn’t be complete without it’s frustrating end.

Over the last year or so, the founding group at StatsBomb have had challenges figuring out the right path forward for the company.

Seven years are a lifetime, especially in a high stress environment, and people’s priorities and outlook changed and what they wanted from the company changed. We didn’t see eye to eye about strategy, risk appetite, and on core values and principles.

As a result, we decided to end our partnership as a group, and seek out new ownership*. I’m sure one day I’ll write a bit more about the lessons learned from the ending, but now seems like a better time for reminiscing and celebration!

I will forever be grateful to the founding group for all the battles we’ve taken on together. This life changing experience wouldn’t have been possible without them. It is rare to find people who would have taken a chance on a random 22 year old halfway across the world.

*Update: https://www.businesswire.com/news/home/20240812879329/en/Hudl-Strengthens-its-Professional-Sports-Solutions-with-Strategic-Acquisition-of-StatsBomb

Some Cliches

Here are a list of cliches I’ve learned that I hope I remember for any future endeavor:

  • The internet is massive and still underrated. You can meet anyone, start any business, be anyone
  • Cold-emails can change your life

This company wouldn’t exist without some very timely cold DMs and e-mails. Hesham found me online because we both wrote at KingFut. I cold-email Ted because I read the StatsBomb blog.

  • Courage is probably the most important attribute for an entrepreneur
  • Do things that don’t scale (link)

if we were more smart about AI and less courageous we would never have started a manual data collection center

There’s more to unpack here – I’m sure I’ll write more eventually – but I think this is a good summary of quite a crazy story that I was lucky to be a part of.

Syntax is for Suckers

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Back in 2010 when I was applying for colleges, I was asked by UChicago to write an admissions essay* with this prompt:

"Dog and Cat. Coffee and Tea. Great Gatsby and Catcher in the Rye. Everyone knows there are two types of people in the world. What are they?"

I ended up writing an essay rejecting the prompt – claiming that this binary categorization failed at being interesting and useful in anyway:

But if you ask this question to my peer group, the repeated answer that crops up is engineer/non-engineer paradigm. Perhaps this is driven by cultural reasons (Eastern culture really love their engineers) but more fundamentally people really do think that the world can be split into groups that have high analytical skills and groups that don’t.

Intuitively, I understand the obsession with engineers. Building and creating products (whether real-world or digital) is a form of magic, and as a result society has rewarded those magicians handsomely in the form of cultural approval/money.

What people mean when they say engineer vs. non-engineer can be re-framed in the 21st century as builder vs. non-builder or creator vs. non-creator.

But it never made much sense to me. Even at the young age of 16 writing that college essay, I rejected the premise that the two groups were fundamentally different.

I believe people are inherently creative – they just don’t know how to create. The means of creation aren’t easy and malleable enough yet.

A good way to think about this is to think about time-travel. A great architect/civil engineer today would probably struggle if dropped in the 1500s and tasked to build a home from scratch. The tools have advanced so much that the exercise itself has no resemblance to it’s past.

There would be more good data analysts if people had the patience and skills to clean data.

There would be more good designers if people could get over how intimidating Adobe tools are.

There are more good story-tellers than there are good writers because the medium and tools are difficult. We haven’t really figured out how to turn great verbal communication into great writing. (Maybe not a great example because the limiting factor to writing seems to be intelligence & discipline not grammar & keyboards).

In most creative/building fields, a sizeable portion of the work, maybe even a majority of the work, is just learning the tools. It’s learning the syntax.

The Syntax for data analysis is Excel, R, SQL.

The Syntax for graphic design is Photoshop, Figma, XD.

The Syntax for video editing is Premiere, iMovie.

The Syntax for creating code is VS, Git and AWS.

The limiting factor is the tool. The simpler and easier to use the tool is, the more adoption for the underlying activity. Excel has done more for data analysis than math classes ever could.

(Elon had a great bit on Joe Rogan about the “data rate” being the limiting factor and eventually communicating via brain waves that relate to this concept)

As a relatively tech-literate and analytical person, I was shocked at how much this premise was true in the software industry. The closer we got to artificial intelligence and digital products, I expected these syntax problems to be solved.

But as a person who has attempted and failed to learn to code as well as design software products better multiple times over the last few years, it’s shocking what percent of the learning curve is syntax. It’s even more shocking when most of this syntax in 2022 is now useless. Coding languages and workflows are built for massive organizations that may care what version of Python they use – as an amateur who wants to build simple apps that are mostly glorified spreadsheets – I don’t actually care.

Coders typically have to work across at least six different products (code editor/IDE, hosting the code, connecting APIs, hosting the site, control log data, and get help on stack overflow)

This is one of my core beliefs. Companies that improve or destroy the syntax for creative tools will rule the world. They will also kick-off the golden age of creation, where the limiting factor for creatives will just be initiative and discipline.

I’m a massive believer in company’s where the core goal is expanding the target market for a high-value activity. Instead of building tools for the existing audience to get better at their jobs, they’re moving the goalposts and re-imagining what the actual limiting factors are.

No-code tools like Bubble, Webflow, Zapier & Airtable that make it easier for non-coders to build apps.

Diagram which automates Figma tasks and workflows to help designers focus on just designing.

Even teaching platforms like Lighthall, who are expanding what it means to be a teacher.

And the leading contender in the space and one of my favorite products of all time: Replit.

Replit’s goal is to make the ability to write code as easy as the ability to write**. 

It basically does three unique things:

  • It’s fully online. You can use it from any computer that can connect to the internet and run a web browser, including a phone or tablet.
  • It’ll fully manage your environment for building and running code: you won’t need to mess around with making sure you have the right version of Python or the correct NodeJS libraries.
  • You can deploy any code you build to the public in one click: no messing around with servers, or copying code around.

They’re also messing around with cool AI to help explain code better to beginners.

The best thing about Replit is that it works and works well. The next best thing is that it actually does what it’s aiming to do: expand the target market. 50% of Replit’s users are under 18, which is a massive number and speaks to how much the product resonates. It’s also a risky business strategy, young users that stick probably have ridiculously high long-term value for Replit but their current value as hackers/coders is much lower than the enterprise coder.

Replit makes me wish I was 18 again, with nothing but free time and the ability to spin up code without spending 20 hours figuring out why my Windows won’t work because of an OS issue. Or because of a syntax error in Python. Because Syntax is for Suckers.

In a weird way, my obsession with this space has helped me redefine what I’m passionate about. If the answer to that question previously used to be “I like helping coaches and athletes use data to understand their sport better and how to improve”, the newer version of this is

“I like building tools that help people maximize their talent” I think everyone can learn to be an analyst, an athlete, and thanks to companies like Replit, an engineer – the oldest form of magic.

* UChicago are famous for all kinds of weird essay prompts. Check them out here

** Paraphrased from this wonderfully long profile on notboring

He Should’ve Squared It – Quantifying Bad Shots

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The problem:

I’ve written before about decision-making in sports, and how difficult it is to quantify.

It’s actually something I’ve been thinking about for a long-time, evidenced by this tweet in early 2020.

I’ve attempted half-hearted solutions code solutions to this that never quite felt right (mostly because passing probability models aren’t good or granular enough), until I got my hands on some StatsBomb 360 data.

The Solution:

With 50 or so odd games of 360 at my disposal, I present to you my newest metric:

Ross Barkley or RB’s.

A Ross Barkley is how much xG you gave up by shooting instead of passing to a better positioned teammate.

(xG of Potential Shot * xP of pass) – Actual xG of shot = Ross Barkley’s

I built a passing model with my limited 360 data and combined it with SB’s freeze frames to quantify:

Actual xG = xG of shot

Expected Pass(xP) = Probability of completing a ground pass to teammates in freeze frame given defenders position (built by a sample of 360 data)

Potential xG = pxG if the receiver of potential pass shot immediately upon getting the ball – and assuming no-one else moved in the freeze frame!

More RB’s are obviously not good as it means you are consistently ignoring your better positioned teammates.

Results:

Here are the five biggest RB’s in the first 15 weeks of the EPL:

Stuart Dallas with an extra touch instead of pass to Bamford – this one works although not sure the pass is 80% expected completion by the time he shoots

Actual xG: 22% , Potential xG: 87%, xP: 80%, RBs: 47%

Saka with a bad decision here – Bellerin pass is on – it’s good to see Bellerin’s bad reaction here confirm what we’re looking for – although I think the model wants him to pass it to Laccazette (and that is definitely not a 78% pass)

Actual xG: 6% , Potential xG: 81%, xP: 78%, RBs: 57%

Pretty good example here – Fabio Silva has a tap-in if the cross goes through

Tough pass here for Bowen to Antonio but probably worth it – pass model looks a little too ambitious though!

More frustrated teammates from Grealish here- a great sign!

In general, it looks like the concept works well despite xP being too high for my intuition – they’re all shots in chaotic situations – after a dribble or coming from a first time pass that probably should’ve been passes.

Some musings on what this all means:

A quick look at some aggregates confirms that RBs have an inverse relationship with xG/shot – which makes sense – if you’re already shooting high quality chances you probably don’t have a bunch of better options.

The biggest offender and outlier to this rule?

Jack Grealish – who has combined RB score of 4 xG – meaning he could’ve created 4 more goals if he wasn’t so selfish.

Interestingly Grealish has the highest xG/Shot of all the top offenders – and given what we know about the load he carries (this great piece by Euan on Villa 20/21 is a must-read) perhaps it’s natural that he ends up taking a few shots at the end of high difficulty carries.

The most selfish players:

Jack Grealish
Grady Diangana
Mateusz Klich
Alex Iwobi
Wilfried Zaha
Arthur Masuaku
Josh Brownhill
John Egan
James Philip Milner
Matty Cash

On the other side of the spectrum – the only players with a xG/Shot of less than .2 and still making good decisions are Harry Kane, KDB and Firmino – and interesting group of superstar attacking players all known for good decision-making!

The least selfish players:

Patrick Bamford
Mohamed Salah
Tammy Abraham
Dominic Calvert-Lewin
Trent Alexander-Arnold
Harry Kane
Alexandre Lacazette
Kevin De Bruyne
Callum Wilson
Ollie Watkins

Apologies to all England fans for that list – Harry Kane really should’ve squared it to Sterling:

Obviously this is all somewhat linked to team style, context, and the models (specifically the pass model) are still pretty weak and can be much improved by people with better Math #skillz.

Also important to note that the good people at StatsBomb I work with will probably produce something far superior to this – this is just me playing around with some data!

Methodology:

The passing model had two features in my efforts to keep it simple.

One was the average distance by all defenders in the frame to the pass path.

The other was the sum of the distances of defenders in ‘relevant distance’ defined as the players who if at top speed (30 km/h) could make it to the interception point of the pass before the pass crossed it.

For xG numbers – I just stole from our great Data Science team their model in API format.

Flaws/future improvements:

The biggest flaw in this model is the assumption that by the time the pass gets there the defenders and keepers won’t move to adjust for the potential shot. Could handle by building some sort of optimization for the defenders to try to minimize the xG of the shot and assume they have a top-speed of 30km/h or so. Quite a challenging problem though because defending for the shot could open up other easy passes.

Passing model is quite weak – the proxies obviously not working very well. It also does not deal with pass height, run-on/through-ball passes at all as some passes are quite easy if you incorporate height or empty field space. It also does not handle offsides. Both are solvable problems that need more thoughtfulness. Voronoi diagrams and exit velocity and angle of pass perhaps a better modelling input.

Assumption of first time shot is a weak one, sometimes the pass is coming from straight behind the potential shooters and so his body position is all wrong. This could be improved by angle of pass or proxy for body position maybe.

What was he thinking? Decision-Making in Sports

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Whenever you watch any sport you quickly hear the commentators talking about concepts such as IQ and decision making in regards to player.

But despite the boom of event and tracking data we don’t really have great metrics to capture or understand good decision-making. This isn’t just because our data isn’t good enough, it’s because the concept of an optimal decision is so context specific in free flowing sports that it quickly becomes a very difficult disentanglement problem.

So let’s try to define decision making is first:

An optimal decision is the one out of your possible options that best helps your team achieve their goals

Possible options is a function of two things:

What do you perceive as an option?

Simply put, a teammate you can’t see or don’t know is there is not an option to pass to

What do you think your team can execute?

A very difficult pass with your off-foot that you know you have 0% chance of executing and therefore pass up could be a good decision

Obviously goals usually differ in sports depending on time and score but for simplicity let’s say the goal is to maximize goal difference.

As a result of all of the above, decisions are really hard to measure due to how context-sensitive they are. An example that may further clarify this concept:

  • Busquets missiles a very fast pass to Messi – he knows Messi can control it. If this pass were to someone else, it would be a worse decision

Some output statistics hint at decision-making – things like the rate at which you turn good positions into shots or shot-assists, passing success rate, key passes to turnover ratio, but nothing not tremendously flawed and context specific.

So in the absence of good tracking data, where else can we turn to attempt to quantify decision making? Below are a couple of ideas I had when thinking about this problem.

Freeze-frame shot taking

Using StatsBomb’s FF data to quantify what was a “bad shot” relative to options.

Perhaps build a pass difficulty assumption and take a look at scenario’s where a pass and first-time shot from teammate would’ve been higher expected value for the team.

Next action success rate

If it was a good decision to pass the ball, it follows that what happened was more likely to be a successful event even when adjusting for it’s difficulty – and the quality of the subsequent decision.

Receiver’s pass % above expected (or above personal baseline even).

Turnovers that lead to counter-attacks

Quantifying bad turnovers as one’s that lead to counter-attacks or quick ball progression against your team.

I might take a crack at trying to code some of them up next weekend!

Launching ArqamFC

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This post was originally posted on Medium in 2017

In 2013, I was offered the chance to go the Sloan Sports Analytics Conference as a student. I ended up missing the conference but live streaming all the talks I was most interested in. It was my first experience with the formal field of sports analytics, and I think it was at that moment I knew that I wanted to contribute to the field.

4 years, 2 jobs, and countless hours later I’ve finally decided to dedicate my time to building a company that changes the way business is done in the sports space.

So we are very proud to announce after 18 months of working informally as hobbyists, we’ve finally launched ArqamFC.

ArqamFC is a data analytics firm focused on bringing the first layer of intelligence to football in emerging markets. We collect, analyze and present data to fans, clubs, players and agencies.

The central thesis is simple. Football is one of the biggest yet most inefficient markets in the world. From the way we talk about it as fans, to the way decisions are made, football is still in the stone ages when it comes to analysis. We are still stuck in this paradigm where most analysis is done through watching, and video cameras are the biggest tech revolution the sport has seen.

The problem with using our eyes to tell us what happened is twofold:

  1. It doesn’t scale
  2. It’s full of all sorts of cognitive biases

We’ve been around for 18 months now, worked with coaches, created content, and generally done whatever we thought would be the best use of our time. We have been held back due to a lack of data however, which is why in January we decided to launch our own data collection center. As a result, we’re finally at the point where we can do whatever we want.

So what do we want to build?

Well, first and foremost we’re a content company. And whether our content drives more fan arguments, or results in a team making a $10mn decision, it’s still at the core of what we do.

Now in order to become the best version of ourselves, there’s going to be a lot of technology that needs to be built:

  1. Data collection technology
  2. Data analysis tools
  3. Data visualization tools
  4. Distribution/user experience tools

With these tools and content, we hope to empower all the relevant stakeholders.

Fans who want to make more intelligent arguments

Coaches who want to prepare better for big games

Agents who want to discover young new talent

Players who want to improve their game

Mostly though, I’m excited to build products and find new and different ways to monetize them. And I truly believe what we’re doing can be transformational for specifically the lower/middle echelons of football, where mistakes can’t be wiped away with huge bankrolls and competitive edges are smaller.

So please check out the website, give us comments. We’re hiring a lead engineer, so recommend all the rockstar techies you know. I appreciate all the support in advance 🙂

One last paragraph for quick acknowledgements.

My partners, Hesham and Ossama have been invaluable as always in getting us to this point.

The team over at BECO Capital, who have given me an invaluable working experience over my last year.

The good people of Footy Analytics twitter, who provided the platform for me to learn about this cool space. (Specific shout out to Ravi Ramineni and Micheal Caley, who answered the DMs of a 19-year old kid in college trying to learn with much patience).

And my family, blood and chosen, who have helped make my decision to become self/unemployed manageable!

The Disruption of the Sports TV Experience

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I’ve been thinking a lot lately about the news that NFL ratings are down, as well as EPL ratings (and potentially MLB ratings?). As a sports fan who follows the NBA, the NFL, EPL, La Liga, and the Egyptian Premier League avidly, it’s always interesting to compare sports media across sport, geography, and quality.

The more nuanced conversations about have revolved around a couple of key factors:

  • Cord cutting
  • Competitiveness of the leagues
  • Quality of play
  • Over-saturation
  • The rise of e-sports

And while some of the wider points made above stand, especially for specific sports (see: Mark Cuban’s “hogs get slaughtered” comment about NFL), I think there may be an underlying issue that’s being missed here: TV as our user experience + distribution medium.

I may be biased due to a) being a techie working in VC and b) founding a startup in this very space, but I believe the most pressing issue for TV ratings is the TV itself as a medium, and networks approach to just being a distributor.

To set the scene a little consider this:

The only group that is watching more TV is above 50 in age. Otherwise, TV usage is down across all ages, and above 40% decreases in the cohort of people under 24.

The ironic thing is, sports have largely escaped the disruption that has faced other entertainment verticals such as music and TV due to one important factor: urgency. Most sports fans have continued watching TV due to the fact that they need to consumer their sports live, a beautiful little feature that has allowed TV ratings to continue to skyrocket over the past couple of years despite the above chart.

But why has this finally hit live sports now? I think the answer lies in the evolution of the “second screen” experience, and how football games 90 minutes + studio are becoming unbundled.

20 years ago, on a Saturday/Sunday your average football fan would open up the TV in order to watch his sport of choice.

The experience entailed the following:

  • Pre-match studio preview (typically from famous ex-pros)
  • The actual broadcast w/ live color commentating
  • Half-time analysis from the studio & highlights
  • Post-match analysis with soundbites from players and managers

Fast forward 20 years and the TV experience has largely stayed the same, adding on a couple of nifty infographics and video tools for the ex-pro analysts to play with.

The overall experience however has shifted massively, as fans now have another screen in their pockets that they’re typically glued to during football fans, and they have services that have attacked each of the above bullet points.

  • Pre-match infographics now from whoscored/squawka
  • Commentary is now via twitter
  • Highlights are via giphy/twitter
  • Infographics/stats are from Statszone
  • Post-match tactical analysis from Youtube channels like BBallbreakdown or websites like Spielverlagerung.com or even classic like Barnwell/Lowe

At first, these services were called second screen services because they were believed to be complementary to TV viewing. But as mobile internet usage has just passed desktop for the first time ever, it’s time to start wandering if the second screen has started unbundling the first.

The TV experience has remained stagnant, as shown above, because TV networks thought they were in the business of distribution, not content creation. They’ve saddled us with the same riddled cliches, the same old faces, and poor/inaccurate commentary. I still can’t choose camera angles for the games, I can’t choose different commentary, I can’t incorporate stats/infographics on 1 screen with video.

With all the cool things I can do on my mobile, combined with the fact that the only reason I own a TV is to watch my live sports (as a 22 year old I fall on that purple line with the steepest decline in TV consumption) and you have a real problem on the networks hands.

It’s fair to say that if Twitter had rights to the sports I loved I would 100% watch it on Twitter due to the ability to have my curated commentary instead of the networks. And the success of the NFL’s twitter deal does drive home my point that the delivery mechanism (TV) is failing networks.

Lastly, I do believe it is also relevant to talk about the actual leagues themselves and how they need to adjust to this new reality. Over saturation is a real thing, and for someone that follows 3 sports across 4 countries and 3 continents, I can tell you right now: there’s no time in the schedule to keep u with all the quality football/NBA/NFL happening at the same time.

The issue for leagues is with the abundance of sports (and the rise of e-sports) available, it is difficult to buy 90 minutes (or in the NFL’s case 3 hours) of anyone’s undivided attention. I would wager heavily that engagement while watching sports have fallen drastically, and this tweet by Mathew Ball seems to confirm it:

Specifically for football (the real kind), 90 minute matches often have around 40–50 minutes of real action. NFL games are more commercials than plays and anyone who has watched the last 4 minutes of an NBA game knows it’s closer to half an hour. Why is this a problem? Because often times watching GIFs on twitter is more than enough to see all the relevant pieces of action from any one sporting event. It’s why the NFL is cracking down on GIFs, and it’s why I love NFL gamepass so much (for the no timeouts, no commercials options).

The first loser from this are local lower quality leagues (like the Egyptian one), where the average fan will choose Arsenal vs. United 99 times out of a 100 to a top of the league clash that isn’t the Cairo Derby. I tweeted about this a week ago, but some innovation is needed on both the product itself, as well as distribution in order for lower leagues to stay competitive.

The NBA has done the best job embracing the smartphone as the center of the new universe, and I don’t think it’s a surprise that they’ve had the least problem with TV ratings for now.

But they have embraced Vine (R.I.P), league pass just launched mobile-friendly view, highlights are free on Youtube, and the official site has a couple of cool content creation tools for stats, etc.

With networks locked into some of their core sports contracts for long time periods, the question remains: will they able to adjust and innovate to the new-age of consumer, or will the second screen overtake the first?

Edit: The elephant in the room for networks is that the big tech companies will come for these contracts, and will build immersive experiences around sports, which makes their position even more precarious