u/URThrillingMeSmalls

When training a team, not every position has the same physical demands. When it comes to sprinting, it is one of the most important physical characteristics but also one of the most injury prone.

I did a project where I took professional data and ran some data science algorithms to figure out which positions had similar types of sprints. In that way, we can segment our players and give them training regimens that help their growth.

The video I included is for a center forward. The data showed that there is usually a chaotic phase, then a sharp cutting phase and then a large distance sprint. This is a segmentation of that sprint so that we can periodize the training.

I took real data and mapped all runs above 5.5 m/s per position and type of event.

This clip I included is a Right Center Back while in possession of the ball. What I found was that not all high speed running and sprints are the same.

I included small amount so we can see that most runs are not linear. Adding different types of sprints are crucial for training to be better but also injury reduction.

Data Science principals used were cluster - namely PCA - to find features and correlate player positions.

With this knowledge, we can create better training regimens.

This is a cool visual aid I made in python to explain clustering.

I've taken sprints across a game and tried to determine which positions relate to others. I believe this will help with training sprints since there are many ways we can.

Such as short sprints, curved sprints, cutting, plyometrics, etc.

With my latest project, I wanted to see what the threshold for stretching center backs out of shape was. I play center back myself and balls over the fullback, into the channel are my team's worst enemy.

I have to go pressure the ball but it usually leaves a gap between me and the other center back that the attacking team can move into. I measured all distances of two teams and used Q3 + 1.5 * IQR to log "stretching" events.

Here is a clip of a stretching event.

I really like adding time as a metric and using it to create clips. It helps see when variables change in games. This player in particular seemed to really grow into the game in the last 10 minutes. His chart jumps for most variables at the end.

Any other off the ball run metrics anyone can think of adding?

I wrote this code to analyze off ball runs. I took two players who had the highest overall score with these metrics:
- number of off ball runs
- total xThreat
- total xCompletion
- average speed
- number of players bypassed

This player's off ball runs created space for their team. This is an example of a good off the ball run. After playing the ball out wide, they make a run into a space where they are unlikely to get the ball. But it opens up space behind for their team to pass the ball.

This metric is a bit harder to see with just numbers and is a good example of using positional data and video to accompany data.

During a goal, this is how the offensive team's zone control changed.

Red-ish areas are where they lost control and green are where they gained control.

(attacking left to right)

How this happened:

- The ball was moved to the left half space from the right side.

- The attacking left winger moved into that half space

- The defensive team shifted hard to their right side (red square area)

- They gained a spatial control but gave up space in the center

- The offensive team remained in the center

- The ball was played into the box and rebounded to 13

- It was shot at the top of the box and scored

Most of us know about positional play. It has become a heavily discussed topic since Pep Guardiola made it more famous. But how do we measure the ownership of space?

I believe putting voronoi diagram over top of positional landmarks can help us better understand which team is dominating a space. Then we can take the total coverage of a polygon over a landmark to get a percentage.

This can help us design team shapes and player relations to take over pivotal areas such as central lanes and half spaces.

I recently post a still image of a shot and screening shadows. This is a 10 second sequence of when a goal was scored.

As the carrier of the ball moves across the box the danger evolves. Players create screening shadows that block the keeper's view. This creates chaos.

I included in my model

• angle off center of goal
• whether the keeper was in a shadow
• number of blockers
• distance to goal
• percentage of goal blocked

Any other suggestions for variables?

This is a freeze frame right before a shot was taken. There are multiple blockers in the way and their cover shadows are highlighted. The ball was shot inside the box. The ultimate result was "no goal".

This is in large part because the goal keeper has a clean sight of the ball and partly due to the angle of the shooter from center of the goal. These are two metrics in this game that made a big impact on the likeliness of the goal.