Thursday, 25 April 2013

Game States And Attacking Tendency.

One of the biggest challenges in football analysis is to add context to the ever increasing raft of available figures. A team's objective at the beginning of a game will range from securing all three points, if they are the superior side in the match up, to avoiding defeat, if they are the inferior team on the day. How they achieve these various aims will greatly depend on the match situations they face, defined primarily by the current score and time remaining. In short Game States are hugely influential, along with opponent quality in determining the kind of match statistics that a side may record.

The largest, readily available source of match by match data was released by MCFC and Opta last year and contains over 200 categories of in games statistics for each side for every game played during the 2011/12 season. It is therefore, in the absence of more detailed play by play data, an ideal source to test the observation that teams will prioritize different actions during different game states.

The most obvious place to begin is how teams attempt to score their goals, but first we need to quantify a teams current desire to increase or open their account. Current score is a good starting point, but time elapsed is also a major factor. A side's approach to rescuing a single goal deficit will be different if just ten minutes has elapsed compared to a match entering it's final ten minutes. Similarly, a team's inbuilt strengths and weaknesses, as well as their preferred style will also impact on their approach. Finally, relative team strengths add to the game's dynamic. It has been well documented that a mid table team drawing away to a title contender is likely to be a satisfactory game state for the former, but not the latter.

Each of these contributing factors can be broadly described numerically and whilst they are less reliable than using methods such as these, which attempt to track the ever changing game states, they are adequate.

Identifying how content the top sides in the Premiership are with the current score is relatively easy. As with all sides, losing isn't a satisfactory state, (although this is also time dependent) and neither in general is a stalemate for the very best sides. It appears that such sides increasingly shoot from greater distance as these unsatisfactory conditions persist. I therefore compared how the ratio of long range shots to total shots varied by game state on a match by match basis for all EPL teams from 2011/12 to see which sides shared this preferred approach. The teams where the correlation was strongest are listed in the table below.

Swansea demonstrate a tendency to shoot from distance when behind.
Swansea attempted 35% of all their shots from distance in matches where their game state was consistently poor compared to below 25% when they were at their most dominant. Teams which aren't listed showed no game state dependent preference in these particular categories.

Attacking Intentions In Unfavourable Game States For EPL Sides 2011/12.

Sides Which Shoot Proportionally More From Distance In Poor Game States. Sides Which Cause Opponents To Make Proportionally More Headed Clearances In Poor Game States.
Swansea. Spurs.
Fulham. Chelsea.
Liverpool. Aston Villa.
Wigan. Newcastle.
Sunderland. Liverpool.
Spurs. Everton.
Manchester City. Blackburn.
Arsenal Bolton.

In contrast, the second listed sides forced their opponents to make proportionally more headed clearances when they themselves struggled by their own realistic expectations. Possibly indicating a different attempted method to turn around a match centred around more frequent aerial attacks, or at least an approach which  results in more opportunities to loft the ball into dangerous areas of the pitch.

Opponents finding themselves successfully defending an acceptable match position against Spurs had to deal with over 60% of clearances with their head. Those figures dropped to below 20% when Spurs were the team in control of the game.

There may be slight issues of correlation verses causation in these figures which a more granular approach would address, but the trends for sides to alter their approach depending upon games state appears strong, especially as game by game figures have been used. A lack of homogeneity of approach by teams seeking to turnaround a poor situation is indicated, reinforcing an expected lack of correlation overall between individual match events and game results.


  1. Interesting post. Thanks. Justwonder what data you used for it, and where you got it from. I can't see how you could have got it from the MCFC dataset. May be you have access to to the Opta database or have done a huge amount amount of data collection and sorting. Just curious, as I would like to further analyse this topic, but I shudder at the amount of time (and money) the data collection would take before I can even start on the analysis.

  2. Hi Gianni,
    most of the recent game states posts have used just the MCFC data. I've aggregated all of the individual player stats and sorted them into separate matches. The whole process took around three hours,you end up with 380 rows and about 450 columns.
    Lead/draw/lose times are taken from soccerbase. A small amount of the shooting distance stats are self collected. They took a little longer :-).

    Drop me a post if you're having any trouble sorting the MCFC dataset and I'll send you a rough spreadsheet.

    cheers Mark.

  3. Hi Mark,

    Thanks. You seem to be much faster than me on Excel, and data manipulation/collection in general. Still puzzled whether you had enough data to make your points. However, I would be much appreciate if you could send me your rough spreadsheet. Will acknowledge your contribution on my analysis (when I'll get round to it).


  4. No problem, can you send a contact email to my blog email and I'll send out a spreadsheet with the amalgamated data.