If there was one over-arching principle for analyzing soccer statistics, it might be “context is king.” For example, Arsenal’s Bacary Sagna averaged 54.5 passes per 90 last year and West Ham’s Mark Noble averaged 53.2 passes per 90. Intuitively, our first reaction is probably that both players exhibit roughly the same level of passing influence—with maybe the slightest of edges given to Sagna. But we are not controlling for the fact that Arsenal led the EPL with 569 passes per game while West Ham was second from bottom, averaging 326 passes per game. To adjust for this disparity we take each player’s passes per 90 and divide it by their team’s passes per 90, thereby creating a pass usage rate for each player.
|
Passes / 90 |
Team Passes / 90 | Pass Usage Rate |
EPL Rank |
|
| Mark Noble |
53.2 |
326.2 | 16.3% |
1 |
| Bacary Sagna |
54.5 |
569.4 | 9.6% |
142 |
Once adjusted, we now see that Noble was a significantly more influential passer for his team than Sagna and actually recorded the highest pass usage rate of any player in the EPL. It should be noted that usage rates are not a predictive metric, nor are they meant to be, but they are a very useful tool to help us understand a player’s influence on their team and separate “team effects” from individual statistics.
General Usage Rates
Pass usage rate is—to this point—the most widespread usage rate. Devin Pleuler recently published a nice write-up on the subject, which also introduced the idea of network centrality. Pass usage rate is a “general” usage rate in that it does a good job of approximating a player’s general influence to their team. Another general usage rate is the touch usage rate. It differs from pass usage rate in that it measures more actions than just passes attempted (i.e. if a player receives a pass and shoots or turns it over before attempting another pass), so it is potentially a better proxy for general player activity. Broadly speaking, touch usage gives goalkeepers and strikers more credit than pass usage and produces a smoother curve with less variance as a result (see thumbnail below). We have also included Arsenal player’s pass and touch usage rates to further exemplify these differences.
|
(Arsenal, 2013-2014) |
|||||
| Touches / 90 | Pass / 90 | Touch Usage Rate | Pass Usage Rate | Abs. Difference | |
| Mikel Arteta |
95.7 |
80.5 | 12.2% | 14.1% |
1.9% |
| Aaron Ramsey |
98.5 |
77.3 | 12.5% | 13.6% |
1.0% |
| Mesut Özil |
86.3 |
68.6 | 11.0% | 12.1% |
1.1% |
| Santiago Cazorla |
89.8 |
67.1 | 11.4% | 11.8% |
0.4% |
| Tomas Rosicky |
76.7 |
64.1 | 9.8% | 11.3% |
1.5% |
| Jack Wilshere |
82.7 |
63.8 | 10.5% | 11.2% |
0.7% |
| Mathieu Flamini |
77.4 |
63.1 | 9.9% | 11.1% |
1.2% |
| Nacho Monreal |
82.0 |
55.0 | 10.4% | 9.7% |
0.8% |
| Bacary Sagna |
82.1 |
54.5 | 10.5% | 9.6% |
0.9% |
| Per Mertesacker |
61.8 |
48.8 | 7.9% | 8.6% |
0.7% |
| Kieran Gibbs |
76.0 |
47.7 | 9.7% | 8.4% |
1.3% |
| Lukas Podolski |
62.4 |
46.1 | 7.9% | 8.1% |
0.1% |
| Laurent Koscielny |
57.9 |
41.7 | 7.4% | 7.3% |
0.1% |
| Olivier Giroud |
50.4 |
32.5 | 6.4% | 5.7% |
0.7% |
| Wojciech Szczesny |
39.7 |
18.0 | 5.1% | 3.2% |
1.9% |
Attacking Usage Rates
You can measure a player’s attacking influence by looking at their shot usage, key pass usage, and general shot contribution usage. These were the top 10 in the EPL last year.
|
Shots/90 |
Shot Usage (%) | KP/90 | KP Usage (%) | Shot Contr./90 |
Shot Contribution Usage |
|
| Luis Suárez |
5.5 |
32.1% | 2.6 | 20.4% | 8.14 |
47.5% |
| Christian Benteke |
2.8 |
25.0% | 2.0 | 22.7% | 4.82 |
42.5% |
| Marko Arnautovic |
2.9 |
25.5% | 1.9 | 22.2% | 4.79 |
42.5% |
| Wayne Rooney |
3.7 |
26.8% | 2.1 | 18.6% | 5.77 |
41.7% |
| Jason Puncheon |
2.8 |
25.3% | 1.8 | 21.3% | 4.51 |
41.3% |
| Robert Snodgrass |
2.5 |
20.2% | 2.3 | 25.9% | 4.83 |
39.3% |
| Wilfried Bony |
3.9 |
30.1% | 0.9 | 9.0% | 4.84 |
37.1% |
| Kevin Mirallas |
3.1 |
21.2% | 2.4 | 18.2% | 5.48 |
37.1% |
| Philippe Coutinho |
3.6 |
21.2% | 2.5 | 24.3% | 6.13 |
35.8% |
| Rickie Lambert |
3.3 |
23.4% | 1.7 | 16.3% | 5.02 |
35.7% |
Defensive Usage Rates
Defensive statistics remain a relatively under-researched domain in soccer analytics. When people do talk defensive statistics, usually only tackles and interceptions are discussed. I believe this is a mistake, as tackles and interceptions combine to only comprise 24.4% of turnovers. Any overall defensive usage rate should also include clearances and recoveries.
|
2013-2014 EPL Turnovers (By Type) |
||
| Tackle | 11,153 |
12.6% |
| Interception | 10,435 |
11.8% |
| Clearance | 23,459 |
26.6% |
| Recovery | 43,236 |
49.0% |
| 88,283 |
100.0% |
|
Here are the top 10 in overall defensive usage rate in the EPL last year.
|
Turnovers Forced / 90 |
Defensive Usage Rate |
|
| Nemanja Vidic |
20.0 |
15.9% |
| Phil Jagielka |
17.8 |
15.9% |
| Marcos Alonso |
16.0 |
15.4% |
| James Collins |
17.3 |
15.3% |
| Nemanja Matic |
17.2 |
15.1% |
| Martin Skrtel |
18.7 |
15.0% |
| Martín Demichelis |
16.3 |
14.7% |
| Laurent Koscielny |
17.7 |
14.7% |
| Curtis Davies |
16.9 |
14.7% |
|
Youssuf Mulumbu |
17.5 |
14.5% |
It would also be informative to look at tackle, interception, clearance and recovery usage rates, respectively, to get a sense as to a defender’s tactical responsibilities. For example, Nemanja Vidic does most of his work with clearances (11.4 of the 20.0 turnovers he forces) and is responsible for nearly 31% of all of Manchester United’s clearances when he is on the field.
