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Defining
Terms
In a dataset for a football match, certain
actions are fixed, and others a matter of opinion; however, they are fixed by
the referee, which is the first problem.
The whistle is blown for fouls, freekicks, corners and goalkicks are
awarded, offside calls made, cards handed out – all these things happen, and
are therefore included in the dataset.
They may, however, be wrong. This
has a knock-on effect on the dataset; for example, QPR’s disallowed goal
against Bolton in March. Where is that
in the dataset? It was definitely a
shot; it was definitely on target.
However, it was definitely a goal, by usual objective standards – but not
according to the referee-defined objectivity of the game. In recording that action, the observer must
not designate it ‘a goal’, as it wasn’t (although it was); that then feeds into
the realm of opinion in its recording in the shots statistics.
In that realm, there is further confusion; the
recording of shots and shots on target is ostensibly a record of fact, but that
is not necessarily the case. When Emile
Heskey’s attempt against Manchester United sheared off for a throw-in, is that recorded as a shot? He clearly meant to shoot, but if the ball ends up way over there, it can cause confusion –
would any forward (or sideways) pass in the attacking third then be a shot? No,
but only because they are not intended as such. Then we are reduced to considering
the motives of players in performing particular actions, which is frowned upon
in other circumstances, such as when considering whether or not player A is or
is not that kind of player, or when
in ‘but did he mean it?’ situations
(eg Olivier Giroud – 25 secs in, Papiss Demba Cissé, Tim Howard) where it really doesn’t matter if
he did or didn’t, it went in, and thus the result defines the previous action.
A further example of this difficulty is
shown in the different records that may exist for the same match; in the
Manchester Derby, there was much talk of Manchester United not mustering a shot
on target – according to the dataset used in my analysis, they managed 4 shots, 2 of which were on target, and thus did not ‘do a
Blackburn’ (where no shots are recorded in their match v Tottenham). In this realm, therefore, there will also be
variances between different datasets.
Between these two positions are other
actions that definitely happen, but without the sanction of the referee; passes
completed, tackles won, etc. These
simply need to be seen and recorded by the observer and included in the
dataset. This inclusion is however
factual rather than entailing any particular judgment, which is connected to our
second problem.
Which
Metrics? Quantifying Quality
No single metric can define a match; it is
of course getting the goal in the back of
the net that counts, but with the other team attempting to do that too
(unless they are Blackburn playing Tottenham), even goals scored is not
sufficient. ‘Points’ is the final
definer of a result, of course, but is in itself a result of a combination of
actions rather than an action in itself.
Some metrics, such as possession, pass
completion rate, and assists are simultaneously lauded and derided as measures
of quality. The first two in particular
are used to demonstrate the dominance of a team, which mostly works as the
highest performers in these areas tend to be Barcelona; however there is no
causation here (see next section). When
Swansea played Newcastle in April, they had 77% possession, and completed 835
passes to Newcastle’s 181. They also lost 2-0. Thus, high possession and pass
completion rates are useful in terms of potential, but that potential still has
to be realised.
The assist is a tricky beast – and here,
the French refer to a ‘decisive pass’, which seems more useful , as otherwise Hazard’s
rabona against PSG would probably not count as ‘an assist’ as it bounced off De Melo first, before
Roux got to finish – as an assist could be a beautiful piece of individual
skill to set up a tap-in, or just the last mug to touch the ball before the
striker did all the work. The same can
be said of goals, of course, but as they are used primarily as a team-metric,
and to define individual performance only as a subsidiary, this is less
pronounced.
As statistical analysis becomes more
prevalent in the footballing discourse, there is occasionally the feeling that
analysts are searching for more esoteric metrics to distinguish them from the ball in the back of the net crowd. This can make life difficult. An example – shooting accuracy might be
considered a good reflector of quality, but if we look at that metric alone (%
of shots that are on target) a slight drawback emerges (Fig 1).
Alternatively, when Arsenal were shipping
goals all over the place early on in the season, there was still an insistence
that Wojciech Szczesny is a fine goalkeeper (and that David de Gea might not
be). Looking at the rankings for save
rate over the season (% of shots on target against that do not result in a
goal) is similarly surprising from that perspective (Fig. 2 - and Manchester United are
at the top of this chart).
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| Fig. 1 - Best Shooting Accuracy by Team |
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| Fig. 2 - Worst Save Rates by Team |
If no single metric can stand alone in
match analysis, a combination of metrics may be more useful. However, none can define success.
Cause
and Effect – Prophesying the Past
Win
more corners and you’ll win more games, as,
hopefully, the saying doesn’t go (Fig.3). Statistical analysis can assume causality
from a metric that is actually an effect (attack more, and a team is more
likely to win corners – they are also more likely to win; both are results of
attacking more, but also then used to define the level of attack, circular reference warning ahoy). Analysis can be dependent on results, and the
interpretation of the metrics in the dataset behind that result can therefore
change to fit the narrative, eg, Barcelona won because they had more
possession, Barcelona lost because they didn’t capitalise on their possession. The
second statement (guess which match) is more accurate, and also gives the lie
to the causality assumed in the first.
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| Fig. 3 - Most Corners Won by Team by Match |
It’s the ball in the back of the net that
counts, basically. Preferably the other team’s net.
Conclusions
Statistical analysis can be a useful
addition to match reporting, but to me is more important in perceiving trends
over a season rather than explaining a particular result, still less forecasting a game to come. There are dangers at each end of the scale –
over-reliance on particular metrics and an assumption of causality can lead to
inconsistency as conclusions differ between matches; trying to take everything
into account can render analysis so un-incisive that it is useless (or ends up
being a simple statement of shit we
already knew – you have to take your chances; or, Manchester City shoot quite a lot, Stoke don’t - Fig. 4).
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| Fig. 4 - Highest / Lowest Shots by Team |
There is also the tension between objective
and subjective in assessing the quality of a game – castigating Chelsea for
playing ‘anti-football’ when they just beat arguably the best (subj) team in
the world, by doing what had to be done, or lauding Swansea for playing
beautiful football when they got beaten by Newcastle’s more direct approach, are
two sides of the same coin. A complex
combination of metrics may approach expressing
quality of play, but there is still no number that can adequately describe
Cissé’s goal against Chelsea or Ben Arfa’s runs through confused defences. The beauty of the beautiful game is difficult
to convey other than by the use of the word woof.
There is also luck, of both flavours, and
numerous hypotheticals around that – if
Suarez hadn’t been bullied by a tree as a child leading him to take revenge on woodwork
wherever he see it, if Harry Redknapp
wasn’t using a dartboard to determine where Bale is going to play, if Arsenal had had a functioning set of
defenders throughout the season, well then, things
would have been different. But luck
is a matter of chance. And then there’s
the refereeing – if there was
goal-line technology...
Finally, connected to the causality issue
above, there is the danger of assuming X
therefore Y or relying on preconceptions – under-estimating the other team,
setting up not to lose and then going a goal down, being happy in possession
but failing to take chances. At the end
of the day, it’s the ball in the back of the net that counts – you still have
to play better than the other team.
My name is PhilippaB, and I am a functioning statoholic. But I am striving to be self-aware.
