Profiling MLS Wingers Using Basic Data

Transcription

1 Profiling MLS Wingers Using Basic Data In 2015, Ethan Findlay of Columbus Crew and Fabian Castillo of FC Dallas were voted on to the MLS team of the year for their performances on the wing for their clubs. Anybody who watched the league on a regular basis would have a hard time arguing that either did not deserve this recognition for their fine form. Using data provided by Opta, we are going to show how creating a positional profile for an elite winger is possible with basic level data and a little creative thinking. The Background Both FC Dallas and the Columbus Crew played in a formation throughout the 2015 MLS season, but in two entirely different ways. Columbus are a heavy possession oriented team who split the center backs, push the full backs high and ask the midfield pair to get on the ball as much as possible. They spread you out, create overloads and pull key defensive players out of position. Dallas on the other hand sit very deep, encourage you to push numbers forward and are devastating on the counter attack. Instead of creating overloads, they hope to do the complete opposite, and isolate their fast, strong and explosive wingers 1v1 with plenty of space to exploit. In Columbus system, Ethan Finlay drifts inside allowing the full back to stay wide. He makes aggressive runs between the center back and full back hoping to get on the end of through balls. For Dallas, Fabian Castillo plays as a true winger. Staying wide whenever possible, he wants the ball early, with space to run at a defender and beat his man on the outside. Two completely different systems, and two completely different profiles, with the same outcome goals and assists. Using an evidence-based approach, we can begin to see what makes these two the best in the league and highlight some traits we can encourage in our players.

2 The Task Please download and open the excel spreadsheet provided for this task. As you will see, Ethan Finlay and Fabian Castillo have been highlighted for your convenience. Before we begin here s a few things to consider: The statistics provided by Opta are for our use only, please do not send/share them publicly. All statistics are on a per 90 basis so any numbers you see represent what a player did for every 90 minutes he was on the field Any players playing less than 900 minutes (10 full games) were filtered out of the data. All information provided is based on open play so set pieces have also been filtered out. Step 1: Open a Word, Excel or Numbers document and create two tables with x rows and 3 columns. Next, name the headings like the examples below (don t worry about the colors, which have been included for clarity only). Stat Name Finlay # Finlay Rank Stat Name Castillo # Castillo Rank Step 2: It s time to start investigating the information we have been provided. For now, we are going to focus on the outcome statistics (the result of their style of play) which are columns C, D, E, F and highlighted in blue. Start adding the information to your tables, which should begin to look like this:

3 If you don t know how to sort columns in excel, highlight the column by clicking the letter at the top and then hit the A-Z button Stat Name Finlay # Finlay Rank Chances Created 1.13??? Assists??? 1st Shots 1.52??? Goals?????? Stat Name Castillo # Castillo Rank Chances Created??? 29th Assists th Shots?????? Goals 0.31??? Step 3: Now we have looked at where Finlay and Castillo rank for output (the result of their play) its time to look at how they achieve this. First, lets look at their involvement in some generic possession (touches, passes etc.). For this you will need to look at columns G, H and I, which are highlighted in green. Add these to your tables. Step 4: We are going to repeat the same process, only this time looking at some more aggressive creativity statistics. For this we are going to use columns J,K,L,M,N,O,P,Q and R, highlighted in purple. When looking at column M (dispossessed), remember that a lower number is better. At this point it s important to stress that following this exercise, we will investigate how these numbers can help us, and how this ties into a playing philosophy. For now, we are focusing on collecting some information to help us understand what makes an elite player. Step 5: Our final step is to look at our players goal scoring threat. For this we need to collect the information from columns S, T and U, highlighted in orange. When looking at a players average shot distance (column U), again a smaller number (closer to goal) is better, so rank them in reverse order.

4 Our final tables should look like this: What Have We Learned? After looking at the information provided by Opta, there are some notable similarities and differences between the two players. Here s what I personally feel we can take away from this exercise: Both players score extremely low in the total number of chances created per game, despite being named in MLS team of the year Both players score low in total passes, touches and overall passing % Both players score high in average shot distance from goal Both players score high in the number of touches they take in the opposition box (1 st and 2 nd ) Both players complete a high % of their crosses (is this linked to the number of touches they take in the box?) Both players score high for goals and assists (hence the team of the year selections)

5 How Can We Use This Information? By gathering or being provided information on our players, we can better understand what makes them good. Working on the assumption that they were correctly selected in the team of the year, we can assume therefore that: 1. Total number of chances created isn t as important as the quality of chances 2. Taking touches in the opposition box is more important than general possession and involvement in the game (number of touches/passes) 3. Passing into the box successfully is not as important as receiving the ball in the box 4. Operating closer to goal seems to be linked to a higher crossing % We can also use this information to benchmark performances. For example in an average game of MLS soccer, the two best wingers in 2015 took 1.5 and 2 shots per game. Therefore, is it realistic to expect our players to take 3 or 4 shots? Likewise the two players take on % (rate of success) were both less than 50%. As a coach, it s important to understand that an aggressive winger shouldn t be expected to beat his or her opponent every single time, or perhaps not even half of the time.

6 Creating A Profile For Your Player In Your System As was mentioned at the beginning of this exercise, both players were extremely effective in different ways for their teams. If you as a coach want to play a possession oriented system with a winger who drifts inside, the expectations of your wide player will be different to that of a counter attacking team. Ethan Finlay ( possession oriented) Low involvement in all round play few touches and passes Very few take-ons/dribbles attempted at a low success rate Rarely dispossessed Reasonably high amount of crosses attempted with a high success rate Many touches in the opposition box Few unassisted shots Shoots from close to goal High % of shots on target KPI s 1.5 shots per game 5 touches in the opposition box At least 50% of shots from within the box 3 crosses completed from good locations (coach can record if they believe location was good/poor) 1 shot on target from inside the box Fabian Castillo ( counter attacking) Low involvement in all round play few touches and passes Extremely high number of take-ons/dribbles attempted at a high success rate Dispossessed often Reasonably low number of crosses attempted, but with a high success rate Many touches in the opposition box Lots of self-created/unassisted shots Shoots from close to goal Average number of shots on target KPI s 8 take-ons/dribbles attempted 3 successful take-ons/dribbles 2 shots

7 6 touches in the opposition box 1 shot on target from inside the box How Can We Do This Ourselves? The above exercise was completed using data provided by Opta. If we break down what we need to collect in order to complete this exercise, the results are as follows: Dribbles attempted and successful Y/N Shots location (yds), inside/outside the box, on target Y/N, unassisted Y/N Touches in opposition box Crosses attempted location good/bad (coaches decision) By tracking just 4 key events (dribbles, crosses, shots & touches in box) we can do a very reasonable job of benchmarking and tracking performances of a winger s attacking contribution. Other aspects of a players game, which a coach may feel are important, such a defending, game management or decision-making can also be tracked. For example, as a coach, employing a simple 1-5 rating system for these aspects can prove to be a similarly powerful tool.