Team Radar Score Charts
As a sentence that opening won’t actually mean anything to anybody except me so an explanation is in order. To create these charts I took a number of steps with the intention of ‘rebasing’ performance scores that my EPL model (TPOEM) produced for last season. More frequent readers of my articles will remember that players like Bale, Suarez and Cazorla completely dominated TPOEM stats and similarly some teams, which I describe as ‘busy’ fared much better than others – without necessarily correlating to their league position after 38 games. For example, TPOEM liked Arsenal, Liverpool and Spurs a lot – in part due to the overwhelming scores of the star players noted above – but also because they all engage in a lot of activity on the pitch which is considered more important by the stats TPOEM uses (things like shots on target, accurate passes in the final third and so on). This isn’t exactly fair, because a lot happens on the pitch that fairly simple stats like this don’t capture. BUT, on the other hand, the benefit of using them is that I don’t have to watch 380 games a season to compile a body of evidence that measures performance more objectively than video and it will complement other analysis. And, of course, this type of data analysis can be done in seconds rather than hours, days or weeks. As long as we keep in mind the limitations of what we are looking at we can reduce our chances of being blinded by the results.
For this set of charts, I removed the concept of a team’s total performance score and set it in every match to 1. Then I redistributed the effects of each player on goals scored and goals conceded (in both case a higher score is better) so that they add up to 1 for every team. The result above, is a picture that tries to explain more generally which positions the most important players for each team play. They’re also radar charts, and so my memory of playing the Playstation game FIFA (a few years ago now) gave this exercise a pleasing sense of nostalgia!
The charts could suggest in which positions teams are stronger/weaker, or perhaps the playing styles of each team. Liverpool and Aston Villa, for example, are dominated by the FW position (mainly Suarez, Sturridge and Benteke) – this shows their dependency on that area of the pitch for their performances last season. Everton, on the other hand, have impressive M and AM dominance – suggesting a dependency on a strong midfield over anything else. When looking at these graphs, just please remember that a proportional score of 10% for Arsenal’s defence will not equal 10% for Stoke’s defence, because each team has been rebased from a better or worse score than 1.
The players are shown below. Here we get a visual representation of how TPOEM distributed scores and involvement per player per game, this time broken down by impact on goals for and goals against [again, high scores for both is better/more involved]. This time we can see by eye which players are more/less important to each team and we also get a feel for the concentration of scores in particular areas. Disclaimer: you don’t see how much each player played – for example Manchester United’s Buttner enjoyed some excellent scores for the 382 mins he played, so if you misread the graph he looks to be one of the best defenders United had last season – but really what this is saying is that he was heavily involved when he did play. For that reason it may have been useful to include total minutes/apps but too late for that – use your head!
And as far as GKs go, don’t take too much notice of their results. The sad truth is I haven’t incorporated a decent method of scoring GK performance yet.
Does it surprise you that no team was more reliant on one player than Spurs were on Gareth Bale? Probably not. How about Matthew Upson’s impact for Stoke? Yes, that one is odd, but only because Upson played 1 match for Stoke last season and TPOEM gave him a good score for it (i.e. ignore it! The same goes for Victor Moses in Wigan’s chart and Dembele for Fulham!). Yes, I should know better than to include the small samples.