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‘I didn’t realise the game’s impact for years’: the making of the original Football Manager | Games
If you were a football fan who owned a computer in the early 1980s, there is one game you will instantly recall. The box had an illustration of the FA Cup, and in the bottom right-hand corner was a photo of a smiling man with curly hair and a goatie beard. You’d see the same images in gaming magazines adverts – they ran for years because, despite having rudimentary graphics and very basic sounds, the game was an annual bestseller. This was Football Manager, the world’s first footie tactics simulation. The man on the cover was Kevin Toms, the game’s creator and programmer.
The story behind the game is typical for the whiz-kid era, when lone coders would bash out bestselling ZX Spectrum and Commodore 64 titles in their bedrooms and then end up driving Ferraris around with the proceeds. As a child in the early 1970s, Toms was a huge football fan and an amateur game designer – only then it was board games, as no one had a computer at home. “When my parents when to see my careers master, I said: ‘Ask him if it’s possible to get a job as a games designer,’” says Toms. “He told them: ‘It’s a phase, he’ll grow out of it.’”
He didn’t. Throughout the 1970s, he worked as a programmer on corporate mainframes and for a while he was coding at the Open University. “It wasn’t long before I realised I could write games on these things,” he says. “Actually, the first game I made was on a programmable calculator.” In 1980, Toms bought a Video Genie computer, largely considered a clone of the TRS-80, one of the major early home micros. “I realised I could write the football manager board game I’d been trying to make for years on a computer,” he says. “There were two major advantages – it could calculate the league tables for me and I could work out an algorithm to arrange the fixtures.”
The Video Genie never took off – but then Toms bought a ZX81 with a 16k RAM extension and ported the game to that. “In January 1982, I placed a quarter-page ad in Computer and Video Games magazine and it started to take off,” he says. “I can still remember the first letter arriving with a cheque in it. In the first few months I sold 300 games.”
At this time, the game was extremely basic – there were no graphics, just text. Players picked a team from a selection of 16 and then had to act as its manager: buying players, deciding on a squad, then tweaking the side as it went through the season. You started at the bottom of the old fourth division and worked your way up. Toms wrote his own algorithms to generate fixtures and also decide the outcomes of the matches based on the stats of the teams playing.
“The difficult part was the player attributes,” he says. “I gave them a skill rating out of five, but then I wanted a counter-balance that so you couldn’t just buy the best players and leave them in the side for the whole season – there had to be a reason to take them out. In real football, the more you use a player, the more likely they are to get injured, so I incorporated that. Each player had an energy rating out of 20 – it diminished as he played, and the risk of injury increased. There had to be a reason to bring the lesser players in.”
Toms also wanted to add long-term strategy and planning to the game, and this came in its most popular element: the transfer market. In the earliest versions of the game, you’d be offered the chance to sign one player a week, but that selection was randomised – you never knew who would be available. “Say you get a rating three midfielder come up and you need to strengthen your midfield: do you spend money on that or do you wait for a five-rated player who might not come for weeks? This generated pressure and fun.”
The key problem he faced was memory. The expanded ZX81 had just 16k of it, which made some aspects tricky – including team names. “It was long before all the licensing issues came along,” he says. “My problem wasn’t: do I need to buy a licence to use Manchester United? It was that there wasn’t enough memory to store the name. Every team name had to fit within eight characters, so I chose teams with short names like Leeds – although I did put in Man U and Man C. The players were mostly well-known players of the time – but again, with short names – which is why Keegan is in there. It’s crazy how little memory there was.”
Football Manager was first released in the early days of the gaming industry – copies were sold via mail order or at computer fairs. But by 1982, high street stores started to take notice of the emerging video game sector. “WH Smith got in contact and said, ‘We like your game, we want to stock it’, and they invited me down to London. They eventually placed an order for 2,000 units – the invoice for that order was more than I was earning in a year. About a month later, my girlfriend rang me at work and said: ‘Oh another order has come in from WH Smith, it’s 1,000 units.’ When I got home I realised her maths was pretty crap – it was 10,000.”
Toms left his job at the Open University and set up his own company, Addictive Games. The subsequent Zx Spectrum and Commodore 64 versions of Football Manager came with an added component: match highlights, which showed basic graphical representations of key moments, such as goals and near-misses.
“It was inspired by Match of the Day – they extract the most fun parts of the matches,” Toms says. “I purposely didn’t put a match timer onscreen, so you never knew where in the match the highlight was happening; you didn’t know how close you were to the end of the match, and whether there was time for another goal. This added to the tension – it was a critical part of the design. There’s also a slight pause between each highlight, and that also creates tension. It was very simple but it worked very well.”
The game was a phenomenon, appearing on bestseller lists for years. My friends and I had hours of fun just editing the team and player names. We all remember it now. “I didn’t realise the full impact for years,” says Toms. “There was no internet at the time – although I did get a few letters saying: ‘I played your game for 22 hours straight.’ Or: ‘I failed my mock O-levels because of the game.’” He also knew that football pros were playing, including Arsenal striker Charlie Nicholas and Spurs manager Bill Nicholson, as well as Harry Redknapp, who later had a role as a real-life mentor to a competition-winning Football Manager player in 2010.
Toms wrote several other management games afterwards, including Software Star, a simulation of the games industry. But as the number of Football Manager conversions and updates increased, so did the stress. Finally, he sold the company and got out of games, returning to business coding while travelling the world. In 2003, Sports Interactive, the developer of the Championship Manager series, acquired the name Football Manager and rebranded its own game under that title – and the name lived on.
But the game wasn’t quite over. Ten years ago, Toms got chatting to fans of his original game online and asked if anyone would be interested in a smartphone translation – Football Manager as they all remembered it, with the same basic visuals. The response was positive and, in 2016, he released Football Star* Manager on mobile. Recently, he upgraded it again and released a PC version. “People are enjoying it because it’s easy to play,” he says. “That’s inherent in my design philosophy – it must seem simple but have subtle depth or it won’t retain the interest. I’ve had people tell me they’ve played 500 seasons and they have £5bn in their bank account – the balance is clearly right because even with all that money they’re still enjoying playing. I’ve also had people who played the original buying Football Star* Manager for their kids to play.”
Toms has clearly rediscovered the spark that brought the original Football Manager into the world, 40 years ago. He has long-term plans for Football Star* Manager, and perhaps Software Star, too. “I’ve still got loads to do,” he says. “I’ve got far more aims and ideas than I have time to implement at the moment. I’m not slowing down. I should do, but I’m not.”
Spain records higher adoption of Artificial Intelligence – AI in vacation planning than the European average, according to the 2025 Europ Assistance-Ipsos barometer.
The study finds that 20% of Spanish travelers have used AI-based tools to organize or book their holidays, compared with 16% across Europe.
The research highlights Spain as one of the leading countries in integrating digital tools into travel planning. AI applications are most commonly used for accommodation searches, destination information, and itinerary planning, indicating a shift in how tourists prepare for trips.
Growing Use of AI in Travel
According to the survey, 48% of Spanish travelers using AI rely on it for accommodation recommendations, while 47% use it for information about destinations. Another 37% turn to AI tools for help creating itineraries. The technology is also used for finding activities (33%) and booking platform recommendations (26%).
Looking ahead, the interest in AI continues to grow. The report shows that 26% of Spanish respondents plan to use AI in future travel planning, compared with 21% of Europeans overall. However, 39% of Spanish participants remain undecided about whether they will adopt such tools.
Comparison with European Trends
The survey indicates that Spanish travelers are more proactive than the European average in experimenting with AI for holidays. While adoption is not yet universal, Spain’s figures consistently exceed continental averages, underscoring the country’s readiness to embrace new technologies in tourism.
In Europe as a whole, AI is beginning to make inroads into vacation planning but at a slower pace. The 2025 Europ Assistance-Ipsos barometer suggests that cultural attitudes and awareness of technological solutions may play a role in shaping adoption levels across different countries.
Changing Travel Behaviors
The findings suggest a gradual transformation in how trips are organized. Traditional methods such as guidebooks and personal recommendations are being complemented—and in some cases replaced—by AI-driven suggestions. From streamlining searches for accommodation to tailoring activity options, digital tools are expanding their influence on the traveler experience.
While Spain shows higher-than-average adoption rates, the survey also reflects caution. A significant portion of travelers remain unsure about whether they will use AI in the future, highlighting that trust, familiarity, and data privacy considerations continue to influence behavior.
The Europ Assistance-Ipsos barometer confirms that Spain is emerging as a frontrunner in adopting AI for travel planning, reflecting both a strong appetite for digital solutions and an evolving approach to how holidays are designed and booked.
Photo Credit: ProStockStudio / Shutterstock.com
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Tristan Thompson is well-recognized for his career in the NBA, having played for teams like the Cleveland Cavaliers, the Boston Celtics, and the Los Angeles Lakers, to name a few. He was even part of the team that earned an NBA championship in 2016. But while Thompson’s basketball reputation precedes him, off the court, he’s focusing on his various entrepreneurial ventures.
When asked by Yahoo Finance’s Financial Freestyle podcast host Ross Mac if he would invest his final dollar in artificial intelligence or the blockchain, Thompson picked the industry that’s already projected to be worth $3.6 trillion by 2034.
“You see what Mark Zuckerberg’s paying for all these AI gurus? So I might go AI,” he said (see the full episode above; listen below).
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Thompson has already made AI one of his entrepreneurial ventures with the launch of TracyAI, an artificial intelligence that’s meant to offer real-time NBA analysis and predictive insights.
“Imagine a sports analyst or commentator on steroids,” he explained to Mac. “What I mean by that is having all the high-level analytics that you cannot get from NBA.com and ESPN … the analytics are coming from the professional teams. We have certain data and access to certain companies that only professional sports teams have access to. And I was able to pull that data with my resources and put it into the AI agent.”
Thompson saw the venture as “low-hanging fruit,” as it was one of the few areas he hadn’t yet noticed artificial intelligence being worked into. Though AI is slowly finding its way into the sports industry, TracyAI offers basketball fans access to statistics and projections they may not have had through the typical channels, creating a unique fan experience.
Though Thompson admitted AI has created some of its own controversies, it’s a venture where he’s ready to invest some of his financial resources to capitalize on the industry’s projected rapid growth.
“For me, it’s like, if [AI is] covering so many sectors, how come it hasn’t got into sports?” Thompson said. “This is an opportunity where I can be a visionary and a pioneer … I’ve always had this grind, build-up mentality, so it just migrated easily into Web3. If you look at Daryl Morey, he said he used AI agents to curate his Sixers roster … that just shows you that’s the first domino effect into something great.”
Search the internet and you will find countless testimonials of individuals using AI to get diagnoses their doctors missed. And while it is important for individuals to take ownership of their healthcare and use all available resources, it is just as important to understand the process behind an AI diagnosis.
If you ask AI to figure out what ails you based on inputting a series of symptoms, the AI will use mathematical probability to calculate the appropriate sequence of words that would generate the most valuable output given the specific prompt. The AI has no intrinsic or learned understanding of what “body,” “illness,” “pain,” or “disease” mean. Such practically meaningful concepts to humans are, to the bot, just letters encountered in the training set frequently paired with other letters.
New research on AI’s lack of medical reasoning
Recently, a team of researchers set out to investigate whether AIs that achieved near-perfect accuracy on medical benchmarks like MedQA actually reasoned through medical problems or simply exploited statistical patterns in their training data. If doctors and patients more widely rely on AI tools for diagnosis, it becomes critical to understand the capability of AI when faced with novel clinical scenarios.
The researchers took 100 questions from MedQA, a standard dataset of multiple-choice medical questions collected from professional medical board exams, and replaced the original correct answer choice with “None of the other answers.” If the AI was simply pattern-matching to its training data, the change should prove devastating to its accuracy. On the other hand, if there was reasoning behind its answers the negative effect should be minimal.
Sure enough, they found that when an AI was faced with a question that deviates from the familiar answer patterns it was trained on, there was a substantive decline in accuracy, from 80% to 42% accuracy. This is because AI today are still just probability calculators, not artful thinkers.
Artful medical practitioners see, hear, feel, and recognize medical conditions in ways they are often not consciously aware of. While an AI would be thrown off by an unfamiliar description of symptoms, good doctors listen to the specific word choices of patients and try to understand. They appreciate how societal factors can impact health, trusting both their own intuitions and those of the patient. They pay close attention to all the presenting symptoms in an open-minded manner, as opposed to algorithmically placing the patient in a generic diagnostic box.
Healing is more than a single task
And yet, algorithmic supremacists are as confident as ever in their belief that human healthcare providers will be replaced by machines. In 2016, at the Machine Learning and Market for Intelligence Conference in my hometown of Toronto, Geoffrey Hinton took the mic to confidently assert: “If you work as a radiologist, you are like Wile E. Coyote in the cartoon. You’re already over the edge of the cliff, but you haven’t yet looked down … People should stop training radiologists now. It’s just completely obvious that in five years deep learning is going to do better than radiologists.”
Seven years later, well past the five-year deadline, Kevin Fischer, CEO of Open Souls, attacked Hinton’s erroneous AI prediction, explaining how tech boosters home in on a single behavior against some task and then extrapolate broader implications based on that single task alone. The reality is that reducing any job, especially a wildly complex job that requires a decade of training, to a handful of tasks is absurd.
As Fischer explains, radiologists have a 3D world model of the brain and its physical dynamics in their head, which they use when interpreting the results of a scan. An AI tasked with analysis is simply performing 2D pattern recognition. Furthermore, radiologists have a host of grounded models they use to make determinations, and, when they think artfully, one of the most important is whether something “feels” off. A large part of their job is communicating their findings with fellow human physicians. Further, human radiologists need to see only a single example of a rare and obscure condition to both remember it and identify it in the future, unlike algorithms, which struggle with what to do with statistical outliers.
So, by all means, use whatever tools you can access to help your wellness. But be mindful of the difference between a medical calculator and an artful thinker.
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