Sports writers – at least the good ones – don’t rely on a single method when forecasting a certain result, league champions, top scorers, or other end-of-season results. They consider data, news reports, and personal experience to come up with their predictions.
The data
Across football, basketball, tennis, and other sports, a prediction might start with numbers. In football, writers look at expected goals (xG), shot quality, defensive pressure metrics (pressing actions per 90 minutes, etc), and recent form to gauge whether a side is likely to control matches. Basketball forecasts look at a team’s pace, efficiency ratings, lineup data and injury reports; tennis predictions will factor in serve and return statistics, surface performance and head-to-head records.
These metrics provide a rough probability. They don’t deliver certainty, and writers – like everyone else – are often made to look foolish, but they anchor a view in something more stable than just a vague feeling.
Reporting and context
Numbers can’t capture everything. Writers add context gathered through reporting: training-ground observations, a coach’s comments, locker-room mood, travel fatigue, heartbreak at a cup exit or after a Grand Slam or NBA Playoffs final, and recent tactical shifts that might not be obvious in a season’s summary of data. They generally try to remain neutral and avoid showing their club colours (or player’s colours) too much, but occasionally the cracks appear.
A writer with inside knowledge might know that while a team is looking strong on paper, they’re now unsettled by a change in backroom staff. A basketball team might be resting stars ahead of the playoffs. A world-beating tennis player could be returning from injury with limited match fitness, as was the case with Carlos Alcaraz at the US Open, and a journalist may have more insight to the player’s emotional or physical state than fans.
This reporting, and opinion factor, explains to some extent why two writers can look at the same stats and reach vastly different conclusions. One may trust the underlying metrics; another may be swayed by a player’s body language or comments in the press.
The human element: instinct and narrative
Even with data and reporting, instinct is a part of many predictions. Writers develop a sense, or a gut feeling, about momentum, high-pressure matches, and which players rise in the big games. This isn’t magic but pattern recognition built over years of watching sport.
They might also be influenced by narrative and the discourse on podcasts, TV, and radio. While Arsenal were on a long successful run last season for example, the feeling around them was different to Manchester City who were scraping through certain fixtures (before they picked up with the January arrival of Antoine Semenyo), even if the underlying numbers are sometimes similar in such cases. Writers must decide when narrative reflects reality and when it distorts it before making a sports prediction, or what French fans may call a pronostic sportif.
Collaborative forecasts on publications like The Athletic and The Guardian
Major outlets often move beyond individual opinion by pooling predictions from multiple journalists. The Guardian’s Premier League previews, for example, publish an average of writers’ tips for each club’s finishing position, noting that the figure represents the collective view rather than any particular author’s forecast. This is meant to smooth out individual bias and produces a more rounded prediction.
The Athletic uses a similar model. Its weekly Premier League prediction pieces use a “rotating cast of know-alls” (writers) who submit their picks for each fixture, with results tracked against an algorithm and other benchmarks.
These kinds of collaborative formats acknowledge that no single writer is a demigod who can see the future. By combining multiple perspectives, outlets create predictions that are more transparent and, in theory at least, more reliable than individual guesses.
