Many would agree that Artificial Intelligence (AI) has changed sports in a big way. Tracking systems now record every run, jump, and pass. Coaches use it to manage training, broadcasters use it to tell better stories, and analysts crunch the numbers for deeper insights.
The idea was that more data would make outcomes easier to predict. However, some sports have just really become even harder to call.
How Tracking Changed the Game
Sports used to be analyzed through simple stats like shots, runs, or passes. But with AI tracking, teams now have second-by-second data on speed, distance, fatigue, and positioning, and they can use all of that to train better and reduce injury risks.
That doesn’t mean predicting winners has become easier. Turning thousands of numbers into a forecast of who will win is still unreliable. Data may show who looks fitter or who ran more, but it still can’t factor in those moments of pressure, sudden mistakes, or even unexpected referee decisions.
Why More Data Can Lead to More Confusion
Now, having more detail isn’t always better for match predictions. Models can end up too complex, or they latch onto patterns that don’t repeat. Tracking also varies from one stadium to another, so the data is not always clean. Most importantly, sports are unpredictable because of human behaviour. Players lose focus, coaches take risks, and a lucky bounce can flip results in seconds.
Soccer Still Hard to Predict
Soccer is the most studied sport in the world, yet it remains one of the toughest to forecast. Matches are low scoring and decided by rare moments that are hard to model. Even home advantage, once considered reliable, has become weaker.
Recent studies across European leagues show that even with better tracking, results have become less predictable. Player performance can swing suddenly, and those extremes are almost impossible to catch in advance.
Baseball and Ice Hockey Face the Same Issue
Baseball has the richest database of any sport. For this sport, every pitch and swing is tracked, yet the results of matches can still be surprising. Random hot streaks in batting or pitching can ruin even the most advanced models. Research has shown that Major League Baseball is among the least predictable competitions despite all the numbers available.
Ice hockey is another example. Player movement and puck tracking are also detailed here, but the speed of the game, constant changes, and low scoring make it difficult to predict. Penalties or deflections often decide matches, and those moments are beyond the reach of any model.
Sports Where Tracking Actually Helps and The Numbers Behind It
Not every sport has become harder to call. Basketball has benefitted from tracking because it is played on a smaller court, with many scoring events each game. Models can use the constant flow of data to predict outcomes with better accuracy.
American football has also gained from AI tracking. The NFL’s Next Gen Stats programme feeds into win probability models that line up closely with actual results. The structured nature of the game, with clear plays and pauses, makes forecasts cleaner.
Researchers use metrics like AUC to check the quality of predictions. In soccer, the numbers usually land only slightly above chance, around 0.60. Baseball can be even lower. Basketball models often hit above 0.75, which is considered strong.
This shows that predictability is tied not just to how much data exists, but to the design of the sport itself.
Forecasting at a Pro Level
Researchers use metrics like AUC to check the quality of predictions. In soccer, the numbers usually land only slightly above chance, around 0.60. Baseball can be even lower. Basketball models often hit above 0.75, which is considered strong.
That’s why brands and analytics platforms that focus on data-driven insights, such as 10cricklive.com, have started paying closer attention to how AI tracking changes fan engagement and content quality. These systems help turn raw performance data into stories that fans can understand, rather than trying to sell certainty.
What This Means for Analysts and Fans
For analysts, simpler models often work better than trying to use every data point. Coaches can use tracking to fine-tune training and tactics, but they should always only treat outcome forecasts as estimates.
When it comes to the fans, it could mean they’ll encounter more claims of “guaranteed wins” that are actually less credible than ever. Tracking has given us more ways to understand games, but it has also revealed how much of sport remains unpredictable.
Summary
AI tracking has changed the way we watch and study sports, but it has not removed uncertainty. Soccer, baseball, and hockey still produce results that no dataset can pin down.
But even if basketball and American football show more stability, even there, surprises can happen. The real value of it is still in insight and preparation, and not really in trying to turn inherently unpredictable games into something certain.
