It was like clockwork. Manchester United dropped points against Tottenham and critics were quick to question their effort and application, using the team’s physical statistics to make points.

Speaking on Sky Sports, Jamie Redknapp said: “There’s a pattern I don’t really like when I watch teams, and that’s when – and the stats don’t lie – the other team runs more than you. In the Premier League right now, I feel there are a couple of non-negotiables: you’ve got to run more than the other team if you want to win, or at least get close enough that it doesn’t make that much of a difference. Tottenham ran five kilometres more than Manchester United.”

Physical metrics such as distance run and number of sprints are increasingly accessible and easy to understand, so have fallen into common use. It’s become a common theme that, when a big club fail to meet expectations, pundits and commentators look to their running metrics in search of one catch-all explanation. Liverpool and Chelsea were the targets of similar judgment last season.

But is it fair? How much of a correlation is there between a team running lots and that team being successful? While the numbers may not be complex at face value, answering this question is, which probably highlights that such metrics are so nuanced in a team sport that it probably isn’t best practice to tie too much significance to the topline figures.

The first way we can assess it is by comparing the Premier League table to the rankings of distance run and high-intensity distance. Yes, this is pretty simple and rudimentary, but it’s a place to start.

Last season’s Premier League table hardly mirrors the rankings for distance covered or high-intensity distance per game. Sure, Arsenal and Manchester City were high for overall distance, but the teams who came third to eighth had middling – at best – outputs in this respect.

The teams that finished in the top 10 averaged 4,363km over the season; the teams in the bottom 10 averaged 4,332km. In the grand scheme of things, a difference of 31km between those averages is rather small.

Again, there’s a slight difference between the average high-intensity distance for top-10 teams (338.7km) and those in the bottom 10 (328.4km), but we’re talking about an increase of 3%. Similarly when perusing data from this season, it’s difficult to see much of a link.

Looking at it in relation to the table does ignore that success is relative, however. Of course Burnley were not going to finish seventh in the table just because they came seventh in the distance rankings.

Instead, we can use the Opta supercomputer’s pre-season predictions to give us an idea of what a successful campaign might have looked like for all clubs and whether there was a trend of overachieving teams running more.

Ten teams in the Premier League last season finished at least one place higher than their likeliest final place according to the Opta supercomputer in pre-season, and they averaged 4,369km over the season; the nine clubs that finished at least a place below their likeliest final position averaged 4,308km. So again, there is a difference, it’s just very small at 1.4%.

However, Sunderland (+13 places) and Manchester United (+9 places) weren’t in the top 10 for distance covered but exceeded the supercomputer’s expectations to a greater degree than any other team; Crystal Palace (-8 places) and Newcastle (-6 places) were in the top 10 for ground covered but among the most disappointing teams relative to pre-season expectations.

We can also look at expected points, a model that simulates the goals scored by each side in each match based on the xG value of all shots and then simulates every game 10,000 times to determine the proportion that the fixtures end with a win, draw or defeat. It isn’t perfect because it’s limited by the natural pitfalls of xG, but it does give a decent barometer of performance over a season.

The teams that ranked in the bottom half for distance covered last term exceeded their xPts totals by 0.38 on average, whereas the teams that ranked in the top half for that metric claimed 1.88 points fewer than their xPts on average. In short, then, it would appear there’s very little correlation between success (at least in the ways we’ve tried to depict it) over a long period and running distance.

But that doesn’t mean we didn’t find any correlation across the board. When analysing a host of physical metrics over the course of 2025-26, the one that showed the strongest link to the final standings was distance covered in possession.

It isn’t perfect but there is the hint of a correlation between more distance run on the ball and finishing higher up the table. That doesn’t necessarily mean a certain team finished high because they ran more in possession; it’s likely a reflection of that side having more of the ball owing to possessing better players. It still felt notable, though.

But perhaps the greatest link between physical metrics and success would appear to come on a game level, rather than over a full season. Since the start of last season, there have been 86 instances in the Premier League of a team covering at least 5km less than their opponents in a game. In those 86 matches, the side that covered at least 5km less have won 10 times, and seven of those were expected victories according to the Opta supercomputer. That’s a win rate of 11.6%.

Now, there are some obvious caveats here. Most importantly, 20 of these games involved at least one of the teams having a player sent off, and that can obviously have a big impact on both teams’ physical output.

Excluding those 20 games, we are left with eight wins for the team that covered at least 5km less than their opponents from 66 matches; that means the win rate shoots up from 11.6% to a massive … 12.1%.

When we change the parameters to look at instances when one team covered between 2.5km and 4.99km less than their opponents (but excluding games with a red card), the win rate does improve to 25.8%, though that’s still not especially high. The loss rate is 46%.

This suggests, then, some relationship between the most extreme examples of outrunning the opposition and winning. But we shouldn’t jump to conclusions; we can’t be absolutely sure the additional running is any more decisive than a whole range of other factors.

Physical metrics are also often closely tied to playing style, so one team’s output in a match can directly affect that of their opposition. The reason many like to lean on these figures is probably because, on the face of it, they are so simple to understand and very easy – rightly or wrongly – to attribute to effort, especially if you’re talking about a team that have lost.

But the fact there’s no strong correlation between distance run over a full season and success arguably suggests the answer over the long term isn’t to run your team into the ground, instead knowing when to up the intensity for maximum effect.

Jürgen Klopp perhaps summed it up best right at the start of his Liverpool reign. “We don’t have to run 115km per match to be successful. You don’t have to run like crazy if you have the ball. It is only the start. We must know how to turn the screws in the right moment; better thinking, better timing. But if we play a much better team and you are not ready to run more than them, then you are crazy.”

Pep Guardiola added to that in his final season at Manchester City. “It’s about when you have to run. Running just to run? I can go to the track and ask the marathon guys to play for us. It’s not about that, it’s when you have to do it. Last season when some players ran it was useless. The most important thing is the combination of both things.”

Intent and timing. When the going gets tough, intent potentially overrides all else in the eyes of the average fan. To be seen to be lacking effort, whether that reflects reality or not, is a cardinal sin in a struggling football team. But rarely is “just run more” the answer.

This is an article by Opta Analyst