Die Mannschaft

Analyzing data from the German Men’s National Football Team’s last 20 matches in hopes of identifying avenues for future improvement.

Germany is one of the most successful national teams in international competitions, having won four World Cups (1954, 1974, 1990, 2014), three European Championships (1972, 1980, 1996), and one Confederations Cup (2017).

This past summer, the 2020 UEFA Euro Cup was postponed a year due to the COVID-19 pandemic. In the time since then, as the situation has continued to normalize, the German Men’s National Soccer Team, also known as Die Mannschaft, has been able to participate in a number of friendlies and qualifiers with other national teams. Now that the Euro Cup is on the horizon, I (a long-time German-football fan) recently began to wonder what Die Mannschaft’s performance over the last couple years of games has truly looked like. Furthermore, I was curious to see if any patterns in the team’s recent performance data may hint at ways in which the team can possibly improve its performance, particularly for this upcoming tournament, and particularly in the recent light of FC Midtjylland, a small midwestern Danish soccer club that has recently experienced great success and received wide recognition due to its heavy focus on-data driven football.

To address these questions, I compiled a 48 x 283 dataset comprising a broad range of statistics on Die Mannschaft’s last 20 international matches, such as player ratings, on-target shots, substitutions, etc. In this process, I used R to scrape the initial data from ESPN.com, FootballCritic.com, and Statista.com, then to clean and wrangle the data, and finally to visualize and analyze it. Below are my findings:

Part 1 — Die Mannschaft’s Overall Performance for The Past 20 Matches (3/24/19–3/31/21)

The first thing I wanted to explore in the team’s performance over the past two years was the distribution of both German and opponent goals between the two halves of the game. In doing this, I hoped to figure out whether Die Mannschaft is a stronger first-half or second-half team, both from an offensive and defensive standpoint.

Scoring 0 goals by the end of the first-half 5 times, 1 goal 9 times, 2 goals 5 times, and 5 goals 1 time, the German National Team has scored an average of slightly above 1 goal before the end of the first half over its last 20 matches. This consistency in as low-scoring of a game as football reflects decently well on their first-half offense.

Germany has ended 6 out of its last 20 games with 3 goals, and 5 with both 1 and two goals. On average, Die Mannschaft adds to its goal tally in the second half of the game — similarly encouraging.

The German defense is fairly air-tight in the first half — 15 of 20 opponents ended their first halves scoreless.

Germany’s defense falters considerably in the second half — half of the 15 opponents scoreless in the fist half were able to escape their predicament within the second (some quite drastically).

Takeaway: Germany has maintained a reliably strong offense through both halves of the past 20 games, but they could improve their second-half defense. The next few areas that I was interested in investigating were the relationships between game outcomes and factors such as venue location, team possession rates, and fouls. First, however, it is important to know who Germany played in each of its past 20 matches:

Table showing game numbers (used in following visuals) and corresponding opponents

Venue location did not appear to have any significant influence on game outcomes, with nearly even distributions of losses, draws, and wins between Home and Away venues.

Besides a potential outlying loss to a weaker opponent in Game 1, German losses were characterized by lower than average possession rates. Draws were generally more even in possession, and victories were slightly more one-sided in favor of Die Mannschaft.

This distribution of fouls reveals that Germany’s loss to Macedonia in Game 1 was accompanied by an unusually high number of fouls and particularly yellow cards. Overall, draws saw the most fouls and cards, while victories saw lower levels of foul play.

The index graphed in this bar chart compares a weighted sum of fouls, yellow cards and red cards for Die Mannschaft to the same sum for its opponents, thereby showing the German National Team’s relative roughness in each game. The graph reveals that while the Germans were unusually rough in Game 1, so were their Macedonian opponents. Germany was rougher than their opponents in all draws. Furthermore, in 2/3 of their victories, the German team was less rough than their opponents — sometimes quite dramatically. Perhaps kindness does count after all.

After this, I wanted to do more investigation of relative statistics to get a better sense of how the German National Team more accurately shapes up against its competition, and how this affects game outcomes.

This scatter plot offers a comparison of total shots and saves per game. With more green points in the upper right quadrant, it shows that at least in this sample of 20 games, victory was associated with higher numbers of total shots and total saves, following along a positively-sloped trend line with the approximate formula y= 2.5x-1. The differences between Germany and their opponents’ shots and saves in drawn matches were negatively correlated, almost to an inverse degree, while losses also reflected positively correlated shot and save differences, though with a lower slope.

This scatter plot shows that in German wins in the past 20 matches, differences between German/opponent blocked shot and shot conversion rates are negatively correlated. Thus, in winning games, the more likely Germany was to score on target shots compared to their opponents, the less likely they were to block on target shots from the opponent than the opponent was to block on target shots from them. Again, this trend is true to a much lesser degree in losing matches, and is slightly reversed in draws. This may partially reflect the fact that more on target shots typically means more goals for the attacker and more saves for the defender, however it also speaks to the trade-off between offensive and defensive force in football — suggesting that for Die Mannschaft, risking a weaker defense for a more forceful offense may be well worthwhile at times.

After spending time analyzing Die Mannschaft’s overall statistics, I decided to also take a look at individual player level statistics to see which players make the greatest contributions to the team.

Part 2— Individual German Footballers’ Statistics for The Past 20 Matches (3/24/19–3/31/21)

In comparing average ratings to number of appearances, this scatter plot reveals which players may get too much time on the pitch (potentially M. Neuer, T. Werner, and M. Ginter), who seems to deserves the amount of playing time they receive (L. Sané, J. Draxler, K. Havertz), and who may be in order for more (J. Hector [if he were still eligible], K. Trapp, M. Reus, and T. Kroos).

This scatter plot shows which scorers may be deserving of more on-field time. It is also obviously important to keep in mind that more appearances equal more opportunities to score.

Here we see the players’ involvement in losses and victories. These values should be taken in consideration with players’ total appearances as displayed above.

This plot shows the number of times players have been subbed on and off the field in the past 20 games. Again, these figures mean little alone, but in comparison with the average ratings and total appearances, they help paint a clearer picture of players’ overall standings. For instance, S. Gnabry is a highly-rated, high-scoring, and frequently-appearing player, so his high number of Sub Offs and no Sub Ons demonstrate that he is likely a respected member of the starting 11, who is probably only subbed-off near the ends of matches. On the other hand, M.Reus has only been involved in victories, however with a lower number of appearances, 5 Sub Offs and 1 Sub On, it appears that his position on the team is less certain.

Part 3— Linear Regression on Victories and Losses

Though the many visuals above help to form a better understanding of Die Mannschaft’s past 20 performances and a few patterns that exist between them, conducting a linear regression on victories and losses gives a much more concrete understanding of the variables that have the biggest impact in deciding them. Below are the summaries of these two models:

All in all, relative shot conversion rates and individual blocked shot rates provide the best prediction for whether a game will be a win.

On the other hand, relative shot conversion rates, the number of simple fouls (no cards), and relative pass success rates provide the best prediction for whether a game will be a loss.

Part 4— Conclusion:

In its past 20 competitive matches, Die Mannschaft has again shown to be a powerful force with a W:D:L record of 12:5:3. This being said, the team, like any other, still has its flaws. In order to improve to the greatest extent possible, the German National Team must focus on improving the stamina and resilience of its defense, it should further concentrate on avoiding, rather than engaging or actively seeking out foul play, and while victories do generally necessitate a balanced offense and defense, the team should nevertheless not be afraid of making occasional defensive sacrifices for offensive might, as it has proven to work well for them in the past couple of years. Finally, the team may also eventually want to consider more actively testing younger goalkeepers with much to prove, like K. Trapp and B. Leno, as well as slightly older players who still perform at great heights, such as M. Reus. For Die Mannschaft, the three most important things to focus on are maintaining a high conversion rate, a low number of total fowls, and a high relative pass success rate.