Any statistical analysis of a sporting event can go down one of two main routes. Firstly, they could be raw data, usually expressed as totals or percentages. In football, examples include the number of total corners a team concedes (or forces), the number of minutes a player is on the park for over the course of a season or even how many points a team accumulates. The second format usually depends on rate – goals per possession, corners per dribble, dribbles per game or even passing completion percentage.
Both have their uses, but as football (and other sports) become more and more subject to the moneyball theory and so-called “advanced metrics”, rate has taken precedence over simple raw data. For instance in football, teams play at different tempos – one team may slowly build from the back while other teams favour quick incisive bursts. A team with a “lump it up” and lose possession philosophy is likely to create less chances by dint of not having the ball as often. It’s fortunate we have two very visible clubs with vastly different methods of operating to easily point to: Barcelona dominate possession no matter who they play, while almost any team coached by Sam Allardyce feels more content without the ball than with it.
A Scoring Stats is defined as any goal or assist a player is credited with, therefore a player’s total thereof is the number of goals and assists he accumulates over a season. So far we’ve examined trends throughout Europe, which players were individual total leaders and finally how much the dependence on a particular player varied across the four professional divisions in one country. Now it is time to evaluate which players provided the greatest lift, per game, to their individual teams.
Unfortunately, access to minutes-played data was very difficult to come by, so this is evaluated according to the number of games in which a player participated. In this analysis, only Team Leaders are evaluated (players who led their club in total Scoring Stats) – a follow-up analysis will include all of Europe.
Europe’s Top Team leaders by match
League | Team | Player | Games | % | Stats per Game |
La Liga | Barcelona | Lionel Messi | 33 | 0.516 | 1.485 |
La Liga | Real Madrid | Cristiano Ronaldo | 35 | 0.490 | 1.429 |
EPL | Arsenal | Robin Van Persie | 25 | 0.347 | 1.000 |
Serie A | Udinese | Antonio Di Natale | 36 | 0.538 | 0.972 |
Bundesliga | Bayern Munich | Mario Gomez | 32 | 0.370 | 0.938 |
Serie A | Napoli | Edinson Cavani | 35 | 0.542 | 0.914 |
Serie A | AC Milan | Zlatan Ibrahimovic | 29 | 0.385 | 0.862 |
Serie A | Inter Milan | Samuel Eto’o | 35 | 0.435 | 0.857 |
EPL | Man City | Carlos Tevez | 31 | 0.433 | 0.839 |
EPL | Tottenham | Rafael Van der Vaart | 28 | 0.382 | 0.750 |
EPL | Man United | Dimitar Berbatov | 32 | 0.308 | 0.750 |
Bundesliga | Koln | Milivoje Novakovic | 28 | 0.426 | 0.714 |
Bundesliga | Hannover | Didier Ya Konan | 28 | 0.408 | 0.714 |
La Liga | Espanyol | Pablo Osvaldo | 24 | 0.370 | 0.708 |
Bundesliga | Freiburg | Papiss Demba Cisse | 32 | 0.537 | 0.688 |
Average | Levante’s | Felipe Caciedo | 0.559 |
Complete table can be found at Balanced Sports Scoring Stats page.
Once again, this only goes to highlight how far clear of the pack Leo Messi and Cristiano Ronaldo remain as footballers. While the average Team Leader contributes about what teams look for from an effective, workmanlike striker (the adage goes “a goal every second game” and remember there are probably some relatively below-average Strikers leading their teams here), they nearly triple that average. After them, the next best – the perpetually injured Van Persie – only managed to average one goal or assist per match.
These totals, however, could be swayed for total numbers. Both Real and Barca scored a boatload of goals during season 2010-11. To evaluate the top fifteen clubs by goals-per-game across Europe is telling:
Team | League | Goals Per Game |
Real Madrid | La Liga | 2.68 |
Barcelona | La Liga | 2.50 |
Bayern Munich | Bundesliga | 2.38 |
Man United | EPL | 2.05 |
Borussia Dortmund | Bundesliga | 1.97 |
Arsenal | EPL | 1.90 |
Bayer Leverkusen | Bundesliga | 1.88 |
Inter Milan | Serie A | 1.82 |
Chelsea | EPL | 1.82 |
Stuttgart | Bundesliga | 1.76 |
AC Milan | Serie A | 1.71 |
Udinese | Serie A | 1.71 |
Valencia | La Liga | 1.68 |
Atletico Madrid | La Liga | 1.63 |
Sevilla | La Liga | 1.63 |
This table – comprised nearly completely of the usual suspects – leads us to suggest that while Messi and Ronaldo’s influence is remarkbable, it is in part due to the increased number of goals their clubs score. This of course gives rise to the perpetual (and now, frankly, boring) Messi versus Ronaldo debate and prompts us to pose the “chicken or the egg” question once more – do they top the individual list because of their team rate, or do their teams top the goals-per-game table because of their phenomenal skill? Unfortunately the answer isn’t easily forthcoming but suffice to suggest that both generate remarkable amounts of opportunities for their teams, and benefit from their teammates doing the same.
Finally, a note about VfB Stuttgart. The Reds finished a disappointing twelfth in the Bundesliga this year, yet pounded in 1.76 goals per game without having a player in the top 45 team leaders by rate. Their most productive forward by totals as Martin Harnik with 15 total scoring stats, who contributed to a quarter of their scores (averaging less than one scoring stat every two matches).
Stats for all players, teams and leagues can be found at Balanced Sports’ Scoring Stats page.
Matthew Wood regularly contributes to Soccerlens. Shoot across to Balanced Sports for more of his commentary and analysis. You can also follow him on Twitter – @balanced_sports
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