Intro

This document is a quick look at some visualizations from the 2023-24 Division 1 Women’s Soccer Data. The data originates from the Wyscout database. The visualizations focuse on Xavier University’s season.

Quick summary stats about Xavier’s First Opponents.

## # A tibble: 5 × 5
##   team_name           Average_Pass_Length Pass_Accuracy_Proport…¹ ShotsOG losses
##   <chr>                             <dbl>                   <dbl>   <dbl>  <dbl>
## 1 Chattanooga Mocs                   17.9                   0.737   0.404  0.161
## 2 Dayton Flyers                      19.2                   0.717   0.417  0.174
## 3 Michigan Wolverines                20.0                   0.762   0.321  0.149
## 4 Milwaukee Panthers                 20.6                   0.677   0.418  0.187
## 5 Murray State Racers                20.7                   0.667   0.388  0.188
## # ℹ abbreviated name: ¹​Pass_Accuracy_Proportion

Passing

This part will show a visual about passing.

Looking at completed/not completed passes and how the length of the pass impacted it going to the right person. Takeaway: Xavier should stick to passes under 40 meters and tends to peak at 20 meters distance.

Mapping Analysis

In this part I will create a soccer field and visualize different coordinate data points.

We can analyze where hot spots are for Xavier’s goals off corners. On the flip side we could analyze where opponents tend to succeed on opposing corners etc. Takeaway: Xavier players tend to score off corners on the right post side. Drawing plays to target this zone would be smart.

(“unsuccessful” or missed attempts are currently being removed)

The 45’ - 55’ minute period involved the majority of last season’s opponent goals. Takeaway: Xavier should be more vigilant on defending within these time periods.

We can see in this visualization where each opposing team goal came from against Xavier. Takeaway: is that most came from the left side when facing towards the goal.

Conclusion

This document can serve as a tool for any Division 1 Women’s soccer team for analysis and would just need the proper filtering and pulling of current data to match the correct team and be useful in the future.