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Showing posts with label MCFC Data. Show all posts
Showing posts with label MCFC Data. Show all posts

Friday, 21 September 2012

Bolton v Manchester City. Overall Team Passing Using MCFCAnalytics Data.

In this post  I looked at the individual passing statistics for Nigel Reo-Coker during the first half of the Bolton Manchester City match from the start of last season using the xml data from MCFCAnalytics. Account was taken of the starting and finishing co ordinates for each pass made by Reo-Coker to determine the difficulty of each attempt. Comparison was then made to all the other pass attempts made on the day to see if he over or under performed compared to an overall standard. The higher the expected completion rate associated with an individual pass, then the easier it should have been to complete.


Below I've charted the passing records for each team's starting eleven from the game and compared their actual number of successful passes to the number of expected completions when the difficulty of each individual pass is accounted for. I've also listed the average difficulty of each player's attempted passes. Generally the longer the attempt and the deeper the intended target is into the opponents half of the field, the more taxing the execution. Therefore it's unsurprising to note that both keepers, who hit a disproportionate amount of long balls, overall tried to execute the most difficult passes for each side. Pass difficulty is expressed as how likely it is that the pass is completed, so the lower the figure in the "Pass Difficulty" column, the harder the pass attempt.

Passing Record Of The Bolton Starting Eleven Verses Manchester City.2011/12.

Sorted in descending order of difficulty.

Player. Successful Passes. Expected Completed Passes. Average Pass Difficulty %.
Jaaskeliainen. 21 15.3 37.3
Steinsson. 15 18.9 66.8
Knight. 14 17.5 72.8
Eagles. 29 26.5 73.5
Petrov. 35 38.4 73.8
Cahill. 21 18.8 75.1
K Davies. 19 30.2 77.4
Reo-Coker. 39 41.2 77.7
Robinson. 36 34.2 77.7
Klasnic. 25 27.5 80.7
Muamba. 13 15.7 82.5


Passing Record Of The Manchester City Starting Eleven Verses Bolton.2011/12.

Player. Successful Passes. Expected Completed Passes. Average Pass Difficulty %.
Hart. 15 18.3 52.3
Richards. 29 28.7 71.6
Milner. 52 47.0 73.2
Kolarov. 37 36.7 76.5
Dzeko 20 19.3 77.1
Silva. 61 54.1 77.3
Barry. 44 41.6 78.5
Aguero. 22 24.3 81.1
Lescott. 29 28.2 83.0
Toure. 60 53.5 83.7
Kompany. 35 33.5 83.8

The immediate stand out feature of each table is the under performance against expectation of the Bolton players and the over performance of City. Only four players from the host side beat expectations, while only Joe Hart and the recently signed Aguero fell below average. Numerically, City were also far superior in terms of passes attempted.

Passing statistics are a double edged sword because they quickly provide copious amounts of data ripe for analysis, but they also can quickly overwhelm the senses. A game map containing every pass often merges into a mass of block colour that lacks definition. Even passing wheels for individual players can soon become cluttered and confusing. Therefore in an attempt to mimic the player influence plots, I've tried to produce for each player one single pass that attempts to encompass the essence of his passing contribution in a single game. I've combined the average start and end point for each player's passes in an effort to highlight where each player is seeking to influence the game. In conjunction with the figures in the table above as well as raw passing numbers, we may be able to distill each individuals passing contribution in a few powerful numbers and graphics.

Passing Profiles For Bolton's Starting Eleven Verses Manchester City.

Passing Profiles For Manchester City's Starting Eleven Verses Bolton.




The starting position for each pass summary is denoted by the players name and the length of the line equates to the average pass length. Direction indicates whether a player is passing predominately in field or towards the flanks and in the case of central midfielders their most likely pitch position is used as the origin of the pass.

Hart and Jaaskelainen's plots are similar, but there are subtle differences that do inform. Hart's overall pass length is shorter than his Bolton counterpart and also more pronounced towards the flanks. Jaaskelainen is more route one and deeper, but his overperformance against the expected norm partly justifies this approach or at the very least reveals it as a deliberate and practiced tactic.

Overall both pairs of fullbacks attempted, on average, difficult passes. This is partly unavoidable because many of their pass attempts will have been from the restricted flanks into the more vigourously defended central areas of the pitch and they will probably be longer in length. Steinsson's attempts were generally from advanced positions, but his completion rate was poor, even after allowing for the difficulty of making a completion. However, he fared no worse than many of his colleagues.

As with the keepers, the central defenders appear similar, but Lescott and Kompany attempted much easier and shorter passes from deeper in their half compared to Knight and Cahill. They appear to be primarily defenders who pass the creative burden quickly onto teammates. The Bolton pair in contrast had a much more advanced passing position and chose to play the ball into deeper, more difficult areas. On the evidence of their expected completion rates, Cahill was much more comfortable with this approach than was Knight on this particular gameday.

Toure appears to have principally functioned as a circulator of the ball, his influence was centred around halfway and he attempted and over completed copious amounts of simple passes in a contest where City were never required to ever chase the game.

Milner, Silva and Dzeko's "typical" passes each arrow from wider to more central and advanced areas and are again in stark contrast to Bolton's creative intent where only Eagles performs a similar function. Klasnic and Kevin Davies both make more passes outwards towards the flanks, either by design or necessity. Overall an easier pass to complete, although Davies' completion rate suggests a bad day at the office for the Bolton striker.

Aguero shares with Petrov the distinction of playing the ball on average back towards his own goal albeit from a fairly advanced area of the pitch, possibly indicating an afternoon spent holding up the forward passes.

Passing data often contains much that is merely recycling the ball between players before a more decisive and effective action is attempted. Using an approach that largely cancels out much of this type of pass we can try to illustrate the main thrust of each individuals passing intent and highlight who was trying to do what on the day. By further reference to their actual success rates compared to an league average norm we can then begin to see how successful they were in those attempts, before moving onto more granular details that should highlight individual assists that may be atypical of an otherwise lacklustre showing.

Wednesday, 5 September 2012

How Teams Win from the MCFC Data.

The data released by MCFC, Opta and Gavin Fleig has now been out in the wild for a couple of weeks and it has already been put to good use both in graphical form and as the basis for game by game analysis, most notably by Ravi Ramineni at Analyse Football .

The data as presented in the csv file largely describes the actions made by players in a game. For example the number of forward passes made by Salif Diao for Stoke at the Emirates. One, as it happens. It is therefore a fairly simple task to accumulate match data comprising the total number of forward passes made by Stoke on that day against Arsenal. Once again Ravi's suggestion regarding the use of pivot tables in excel or Datapilot in Open Office is an excellent one.

We can therefore begin to build up a profile of the actions made by teams during games and try to marry these actions to game result to build up a picture of how teams achieve  the results they do. This aim can be best achieved by looking at the stats differential between both teams in the match. Goals are strongly correlated to game success, but as Blackpool discovered, you must also be proficient at preventing goals. The defensive and preventative side of the game can often be overlooked, even though it has a similar level of importance in determining match outcome. In short it is goal difference that is the stronger indicator of success or failure compared to simply goals scored.

Below I've listed the strength of correlation between success over a season as measured by wins plus half draws divided by games played and various recorded events from the MCFC data and then I've listed the correlation between success and event differential. The closer the correlation is to 1.0, then the stronger the correlation.

How Match Events And Their Differentials Correlate With Seasonal Success.

Match Event. Correlation
With Seasonal Success.
Goals Scored. 0.78
Goals Scored/Allowed Differential 0.94
Shots On Target. 0.60
Differential. 0.73
Headed Goals. 0.03
Differential. 0.27
Goals From Corners. 0.27
Differential. 0.45
Successful Passes ex Crosses. 0.54
Differential. 0.54
Successful Final 3rd Passes 0.62
Differential. 0.65
Touches In Opponents Box. 0.56
Differential. 0.73
Shots On Target Inside Box. 0.57
Differential. 0.73


As it's simplest level the differential column now includes the defensive contribution to winning instead of merely the offensive output. Scoring goals on it's own is a major factor for success for a lot of the Premiership teams, but a stronger correlation can be found if we included goals allowed as well. By presenting the wider picture of events we can begin to understand how free scoring Blackpool spent just one season in the top flight and barely scoring Stoke have survived since 2008/09. Concentrating on having a strong defence can be both cost effective and successful and a partial antidote to a lacklustre attack.

The figures, which are far from exhaustive, outline the kind of things the majority of the successful teams excel at over a season. However, care must be taken to avoid making broad statements that do not apply to all teams. Those teams which adopt tactical approaches that are at odds with the majority of other teams will inevitably be flagged up as outliers who have been incredibly fortunate to survive, when in reality they have exploited a niche market that has allowed them to prosper.

Headed Goals...A vital contribution for some teams.


One particularly striking result is the apparent zero correlation between headed goals and success followed by only a slight improvement if we look at the differential between headed goals scored and headed goals allowed. However, if we dig a little deeper, rather than being a worthless artifact of a bygone age, headed goals are actually vital to a minority of teams.

Scoring headed goals is a much cheaper, if less efficient method of moving the scoreboard than taking the ground route. You can create headed chances with little more than tall attackers or defenders and a delivery system, (long throws, set pieces or crosses). Creating Barca style goals from intricate passes usually requires expensive skill throughout the midfield and attacking areas. So headed goals are vital to the prospects of Stoke and Norwich and previously Bolton and Blackburn. These teams make the best of their meagre resources, but are in the minority in prioritizing headed goals both scored and conceded and therefore cannot greatly influence the regression correlation.

Aggregated game stats can shed some light on the type of things some teams are doing and are allowing to be done to themselves over the course of a season. But the picture is broad and sweeping and much fine detail is lost due to lack of game position context and tactical approach of some teams over a season.

By looking at differentials we can strengthen the correlations between season long success, so lastly I'll look at how success as measured by individual wins on a game by game basis relates to positive on pitch actions for each team. Are these broad correlations observed on match day?

Final 3rd completions are widely regarded as the preferred tactical approach for the majority, but not all teams. So it seems reasonable that if this approach is effective, teams will be winning more often if they complete passes in their opponents final 3rd and limit their opponents in this area. Regressing differentials for final 3rd passing, we do find a clear and strong game by game correlation for the EPL as a whole last season. Below I've plotted the line of best fit for final 3rd differentials and the likelihood that the home team won the match.



For example if the home side out passed their opponents by 50 passes in a game, there was a 50% chance that they also won the game and the greater the differential the greater the likelihood that they also won the game. Correlation doesn't imply causation, but it does strengthen the case for final 3rd passes being an important component of some team's armoury. There will of course also be exceptions in this game by game analysis with Stoke, certainly and Newcastle, possibly plotting a different route through a tactical independence from the majority of the rest of the league where the importance of final 3rd completions is diminished.