Increasingly, football is lending itself to plenty of analysis using numbers, but the
availability of data doesn’t always ensure that it’s properly applied.
Here are ten mistakes which are commonly made and the best way to correct them.
The equation for save percentage is shots saved/total shots faced. There’s no accounting
for the difference in the type and quality of shots that a goalkeeper faces, which will
have a large impact on his ability to make a save, and therefore, his save percentage.
Goalkeeper X facing 10 shots from inside the six-yard box is going to have a tougher time
making saves compared to Goalkeeper Y, who’s facing all of his ten shots from 30 yards
out or more.
Expected Goals and its cousin, Expected Goals on Target, tell us that shots from further
out are less likely to result in a goal and shots that are either right at the keeper
or down the middle are more likely to be saved
By equally weighting each shot to calculate save percentage, we are doing a disservice
to Goalkeeper X and making Goalkeeper Y look better than they actually might be.
Instead, comparing the quality of on-target shots by using Expected Goals on Target (or
Post-Shot Expected Goals) to the number of goals conceded, adds much needed context to
a goalkeeper’s numbers.
Goals Prevented tells us how many goals a goalkeeper saved given the quality of shots
he’s faced, compared to the average goalkeeper. Through doing this, as the graphic shows,
Martin Dubravka looks far better than his save percentage says he is, and Vicente Guaita
looks like a world-beater
Premier League clubs have had access to tracking data since 2013-14 and, as part of that deal,
the media get access to derived outputs too. Up to this point, all we’ve really seen
is distance and speed statistics.
These numbers are some of the most contextless around, yet they’re used frequently when
analysing teams and players.
But they shouldn’t be. Firstly, there’s no correlation between the distance run and
the likelihood of winning a game. The amount of distance covered in a finite amount of
time is only useful in a time trial, which football is not. From last year’s UEFA Technical
Report on the Champions League, Shakhtar Donetsk ran the furthest on average of all 32 teams
in the competition, yet finished third in their group and crashed out of the Europa
League in the round of 32. Manchester United ran the second-least on average, yet were
still able to reach the quarter-finals.
Secondly, distance and sprints are stylistic, as in, the numbers that players accumulate
will be linked to what’s asked of them, the system they play in, how the opposition
sets up, game state, and various other factors. Without controlling for — or at least mentioning
— these other factors, these numbers don’t give us much insight.
Finally, there’s also some evidence to suggest that running less actually can be beneficial
— just ask Lionel Messi. Most players have the fitness levels to last a full game but
the manipulation of space is what matters. Similarly, there have been plenty of quick
players to have played the sport but the very best know when to use their pace. Very rarely
do players need to beat another in a foot race but it’s quick bursts of speed to get
past someone or latch on to the end of a loose ball that are key.
As Marti Perarnau puts it in Pep Confidential (“possession is only a means to an end.
It’s a tool, not an objective or an end goal.”
Leicester City won the league averaging 42.6 per cent of possession in 2015-16. Manchester
City won the league last season averaging 67.7 per cent of the ball. In essence, it
doesn’t matter how much you have, but what you do with it instead.
Winning the possession battle doesn’t really tell describe that much beyond how teams set
up to play and in-game, can be entirely dictated by the scoreline. In Atletico Madrid’s 1-0
victory recently against Liverpool in the Champions League. After a fourth-minute goal,
Atleti set up shop, having just 27 per cent of possession. That figure may have looked
entirely different had Atleti not scored early on.
Possession is still a useful nugget of information to understand which side had more of the ball,
but it’s not a statistic with which to argue over superiority. Expected Goals is a far
better indicator of the quality of a team
Not all the defending that a player does is tangible and the measurable output that can
be counted is often biased by team style. Logically, if a team has less possession,
they have more opportunities to defend, and vice versa.
For that reason, tackle and interception numbers are better indicators of defensive style (i.e.
is the player passive or active) and not necessarily the defensive quality of a player. Virgil
van Dijk attempts just 0.76 tackles per 90 minutes, yet no one would make the case that
that makes him a poor defender.
Tackles are usually split into two categories — those that are won and those that are
lost. Winning a tackle consists of a player winning back possession when challenging for
the ball, while losing a tackle sees a challenge take place but without the ball being won
back. Losing a tackle could be due to the ball being poked out for a throw-in for the
opposition, the ball knocked loose for the opposition to recover, or some other reason.
Tackle win-rate is currently defined as tackles won/(tackles won + tackles lost). What this
currently tells us is the proportion of tackles that a player makes where his team wins the
ball back.
Of full-backs in the Premier League with the highest tackle win-rate, Martin Kelly is the
best with 80 per cent of tackles won. The eye test suggests that Aaron Wan-Bissaka should
be amongst the top players, yet he’s only 11th.
But using True tackle win-rate can provide more clarity by incorporating missing categories,
with the equation of total tackles/(total tackles+challenges lost+fouls when attempting
a tackle). Through this metric, Wan-Bissaka is top with a 78.9 per cent true tackle win
rate, and Martin Kelly is down in 29th, which is much more accurate.
When it comes to understanding goalscoring ability, there are two crucial elements that
need to be considered and judged in isolation. The first is a striker’s ability to generate
chances for himself. Goals are a striker’s main currency and to score goals, strikers
need to take shots. To measure the quality of these shots, we use expected goals. If
a player consistently gets into good goalscoring positions, over time, goals will come.
It’s one thing taking shots, it’s another thing to finish them. In small samples — such
as a whole season — a player’s goals and xG may not match up. Take Roberto Firmino.
This season, he’s scored fewer than you’d expect given the chances he has but it is
his best in terms of getting into great goalscoring positions.
Firmino’s three prior seasons at Liverpool have seen him score above, below and on expectation.
This isn’t enough data to give any concrete conclusions on his finishing ability.
‘With or without’ stats are intended to isolate the impact of a single player in a
team to see how results change with that player involved compared to when they’re missing.
These stats can work in sports with smaller segments to analyse such as basketball, which
has more line-up changes and is far higher-scoring. In football, however, there are just far too
many moving parts for this to be a good way of analysing if a player’s any good or not.
There are too many variables: What was the quality of the opposition? What was the quality
of the other players playing alongside the missing player? Was there a red card? Or some
other anomaly.
Instead, It’s better to analyse players within the context of their position and focus
on just what they can control. For creative midfielders, that’s chance creation, for
strikers, it’s goalscoring and so on.
The degree to which a player’s passing is accurate or not depends a lot on what they’re
being asked to do, and the choices they make when on the ball. Some teams, such as Manchester
City, play the ball very short and in certain areas of the field, under little pressure.
Due to this, they’ll have a high pass-completion rate.
Others, like Burnley, look to hit the channels and user longer passes instead of shorter
ones — passes that are, on average, less likely to be completed.
It’s not a descriptive and while Expected Pass completion rates may give a more rounded
view of why a player’s pass completion rates are low, that data is relatively sparse in
the public domain.
An obvious one. Every season, the Golden Boot winner in every league will fail to score
more times than they succeed in doing so.
In most cases, clearly, if the focus is on how many times a player has failed, it’s
worth turning that into a percentage to add more context. Have they failed a lot, or is
it that they’re tried something far more than other players?
Players who play more minutes have more chances to do things on the field that are counted.
By not putting all players on a level playing field in terms of minutes played, it means
that those who have played less will nearly always look worse.
As an example, by adjusting for minutes played, Norwich City’s Emiliano Buendia is actually
the second-best chance-creator in the league on a per 90 basis (3.3 per 90).
Instead, by adjusting stats per 90 minutes played (that is, dividing the stat by minutes
played/90), players who have played differing numbers of minutes can have their numbers
compared, and more fair comparisons
can be made.