Wednesday, 22 January 2014

A Use for 0-0's

Sooner or later anyone who regularly watches football will eventually be treated to a scoreless match. From a spectating point of view, especially for the neutral, 0-0's are often an unwelcome addition to their footballing experience, but from a statistical standpoint, stalemates may provide a valuable baseline into the complex, but increasingly relevant subject of game states.

It is quite natural that a side may change tactics based on their current needs and scoreline at a particular phase of a match. A cricket team having wickets aplenty in hand on the fifth day of a test match, with the winning scoreline tantalizingly in sight may take risks to reach the winning total. At least until falling wickets induce a more cautious, draw orientated approach. Such bursts of accelerated scoring, usually also involving increased numbers of falling wickets, are easy spotted in a sport, like cricket where every potentially scoring action is individually recorded.

In football this ebb and flow in the interactions between teams is less easy to define. Unlike many sports, such as cricket, American football, and baseball, were "goal" prevention and scoring occurs in defined periods of play, scoring and attempting to prevent being scored against occurs simultaneously in football. Retaining possession in football can be both an attacking or a defensive action.

Tactical adjustments based around game state, therefore are likely to be as real in football as in other sports, but even overt changes to a more possession based/risk averse approach when leading may be difficult to spot across a match, especially if the opposition quickly changes their own game state by rapidly equalising a go ahead score.

Stoke and Cardiff Prepare to Serve Up a Statistically Interesting 0-0.
Game state is being increasingly used in football analytics and inevitably the phrase may have different interpretations across different sites. In this blog I have described game state principally as the interaction between the team quality of each side taking part in the match, the current scoreline, the time remaining and any dismissals that may have transpired due to red cards. As a consequence, accurately calculating the game state over even a single match, requires constant re-calculation. Some of the inputs may remain relatively constant, but time elapsed is always moving forwards towards full time.

Thus, goalless games, especially where we have more detailed statistical breakdowns of on field actions, provide the easiest doorway to how sides react in certain game states. A side, especially a talented one often only shows us part of what they are capable of, tempered by what they needed to do, especially if they recorded a fairly comfortable win. For example, anecdotally, 2-0 victories increased in international football when the cast and spread of team quality increased in the 1990's as good teams adopted risk averse strategies in the face of relatively unknown, but probably inferior opponents.

If we stick with 0-0 games, with no red cards, played between teams of known quality, the only major contributor to changing game state that remains is time elapsed. In short, there are no major peaks or falls in game state across the 90+minutes caused by reckless tackles or deflected 30 yarders. Therefore, how game state progresses for such contests is almost entirely a function of the quality differential between the sides at kick off.

The plot above shows how dominant teams were in terms of collecting their share of the total attacking touches of the ball made in the penalty area, during 0-0 matches from 2011/12. The pregame success rate defines how balanced the match was expected to be prior to kick off and red card matches have been omitted. To anchor an example in reality, Spurs would currently have about an expected 0.8 success rate prior to kicking off at home to Stoke.

The trend is well defined, superior pre game sides had the lion's share of attacking touches inside the penalty box recorded across the 90 minutes. For example, the line of best fit gives a side with a pre game predicted success rate of 0.8 an average of 75% of the game's attacking penalty box touches. The longer the game remains stalemated, the more the game state turns against the pre-match favourite, merely through the ticking of the clock, sustaining their efforts to deliver passes and touches in the dangerous area of the penalty box.

The trend in 0-0's spills over into other statistical categories. Superior teams on matchday, on average, enjoyed majority shares of shots, chances created, dribbles, crosses, final 3rd touches and blocked efforts, allied to reduced levels of clearances compared to their inferior opponents.

In short, these historical rates indicate what level of on field actions a typical EPL side is likely to record in a 0-0 match, where the talent gap between the teams is readily known, without intervention from other major game state changing factors, such as goals or dismissals.

If we now wish to see the direction these shared proportions take as factors other than simply time elapsed combine to change the overall game state experienced by each side, we can look at the next lowest match result. Namely, games decided by a single goal.

A single goal victory will improve the average game state of the superior side compared to an identical match that remains scoreless. And similarly, data from single goal defeats should be characteristic of how matchups perform under poorer game states than those experienced in 0-0 games.

How Proportion of Penalty Box Touches Changes by Result & Match-up.

Pre Game Expcted Strike Rate. Game Result
1-0 Win. (Better Game State) 1-0 Loss. (Poorer GS)
0.8 75% 67% 76%
0.75 71% 63% 72%
0.7 66% 60% 69%
0.65 62% 56% 65%
0.6 58% 53% 62%

Above, I've charted the proportion of penalty box touches derived from the line of best fit from plotting graphs for matches that ended in single goal wins and defeats, as well as goalless draws.

In games where the favoured team won by a single goal, their proportion of touches in the area declined compared to the baseline figures derived from a 0-0 result, whether through their opponents becoming more adventurous or themselves more cautious. Where the team lost 1-0, they were good enough and needy enough force an increase in their share of such touches compared to the baseline numbers.

As an example, the best fit for a team with a pre-game expected success rate of 0.7, sees their share of touches in the box falls to a low of around 60% when they win 1-0 and scoring becomes less of a priority for part of the match, reaches a high of 69% when they are chasing a one score deficit and is anchored at 60% when neither side finds the net.

There's nothing new in these conclusions. It has been established that a side that performs poorly by their usual standards over a season, tends to accumulate more products of the attacking football they must undertake to rectify matters than they do in better times. Corners are a prime example. But the use of the 0-0 match as a handy baseline may restore a bit of (statistical) love to a usually underwhelming extravaganza.

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