Monetising misfortune: the financial consequences of injuries in professional football teams

Abstract

Objectives

To evaluate the financial impact of player injuries on team performance in German football’s first and second divisions, focusing on the indirect costs related to reduced performance and lost revenue.

Method

This retrospective, longitudinal study analysed data from seven seasons (2014/2015 to 2020/2021) of the Bundesliga. Injury metrics (incidence, burden and matchday unavailability) were examined using linear mixed models to assess their relationship with team performance (league rank and points). Financial impacts were estimated by translating performance declines into revenue losses from TV broadcasting rights and prize money.

Results

Findings show a significant negative relationship between most injury measures and team performance across both divisions. In the second division, an additional 333 injury days were associated with a one-rank drop, while the relationship was not statistically significant in the first division. For injury incidence, an increase of 4.33 injured players in the first division and 2.64 injured players in the second division corresponded to a one-rank drop. Matchday unavailability had similarly strong effects: in the first division, an additional 1.62 unavailable players per matchday were linked to a one-rank drop, while in the second division, only 0.71 unavailable players resulted in the same outcome. Financial losses due to injury-related performance declines were substantial, particularly for higher-ranked teams in the first division.

Conclusions

Injuries significantly affect team performance, causing notable financial losses through reduced league rankings and points.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• Injuries in professional sports are known to negatively impact team performance and success, with prior studies demonstrating a clear association between injury rates and lower rankings or reduced points in football leagues. Financial estimations of these impacts have focused on direct costs like player salaries, with limited exploration of the broader economic consequences for teams.

WHAT THIS STUDY ADDS

• This study comprehensively analyses the indirect financial impact of player injuries on team performance in the German Bundesliga. Leveraging detailed and longitudinal injury data from official statutory accident insurance records, team performance metrics and financial data, it quantifies how injury incidence, burden and match unavailability correlate with league rankings and points.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• The findings emphasise the economic importance of investing in injury prevention and management strategies. By combining a more sophisticated methodological approach with longitudinal data, this research demonstrates the financial consequences of injuries and advances the understanding of their broader economic impact. It advocates a strategic focus on player health to optimise team performance and revenue generation.

Introduction

Across professional sports, teams invest substantial resources in injury prevention to maintain player availability and enhance performance. However, an economic evaluation of injury prevention efforts is essential for these resources to be allocated efficiently. According to Fuller,¹ such evaluations are critical in determining the cost-effectiveness of these preventive measures and ensuring that the investments yield significant returns. To fully understand these cost-effectiveness analyses, estimating the total costs of sports injuries includes direct and indirect costs. Direct costs often describe the medical costs of injuries. Indirect costs consider that injuries can often cause long absences of players,² and as a consequence, injuries can have a considerable impact on the sporting success of a team.³ For most popular team sports, a large portion of the team revenues is directly linked to their sporting success. With teams receiving money for winning the championship, qualifying for international competition, or playing in a higher division, a reduction in sporting success-related revenues due to injuries can thus be regarded as indirect costs of injuries.

Previous research has well documented the negative effects of injuries on sporting success. Drew et al ⁴ conducted a systematic review and found strong evidence that increased player availability significantly reduces the risk of competition failure. Given the established negative effect of injuries on sporting success, researchers have recently focused on estimating the indirect costs associated with this relationship. One common approach involves utilising player salary data. Donaldson et al ⁵ used this method to estimate the economic impact of injuries in the NHL. Lu et al ⁶ combined the analysis of injuries' effects on sporting success with monetisation through player salaries in Australian professional soccer.

While these studies provide valuable insights, they may not fully capture the broader financial implications of injuries on team performance and revenue, as the financial burden of player salaries represents a passive impact. These costs would have been incurred regardless. Addressing this gap, Eliakim et al ⁷ employed a different approach to monetisation. They assessed the financial implications by calculating the difference between a team’s expected and actual league positions based on the financial value of the squad. This difference was then translated into financial terms by considering the associated losses in prize money, broadcasting rights and other revenue sources. Using this model, they estimated that an average Premier League team loses about £45 million per season due to injury-related declines in performance.

This study contributes to the literature by providing new evidence on the financial consequences of injuries in professional football, using the German first and second divisions. The institutional structure of German professional football, including centralised injury reporting and financial implications tied to promotion and relegation, creates a suitable context for analysing how injuries affect team performance and related financial outcomes. By examining the first and second divisions, the study can explore variation in the financial and performance impact of injuries across league tiers, offering new insights into how injury effects may differ depending on competitive and economic environments. Based on this setting, the study uses a comprehensive dataset covering all professional teams’ reportable injuries across seven seasons. To exploit the longitudinal structure of the data, a team fixed effects model is estimated, allowing for the identification of the impact of within-team changes in injury levels on performance while controlling for all time-invariant team-specific characteristics. Using variation within teams over time provides a more reliable estimate of how injuries affect performance, offering an advantage over cross-sectional or pooled models used in previous studies.

Method

Data

This study is based on data from Germany’s top two professional football leagues for the 2014/2015 to 2020/2021 seasons. German Statutory Accident Insurance for the Administrative sector provided player injury data for the Administrative Sector (Department of Sports Injury Prevention (VBG)). As a part of employee protection, employers in Germany are legally obligated to provide insurance coverage for employees in the event of work-related accidents. The clubs must report any football-related injury to any player that results in the absence of team training or match play. The data were anonymised, and no individual player information was available. Further data concerning seasonal team and player statistics, including estimates of player values, has been collected from the website www.transfermarkt.de. The combined dataset is uniquely defined by season and club ID for the first and second divisions and a total of n=252 observations.

In line with previous studies, a team’s injury rate was evaluated in three ways.⁸ The resulting variables included information about the incidence and severity of injuries. Injury incidence reflects the total number of distinct injury cases (ie, the number of injured players) a team reports throughout a season. The injury burden captures the total number of days lost due to injuries per team and season. Match unavailability is the average number of injured players unavailable on each matchday, calculated across the full season.

The approach used by Eliakim et al ⁷ was adopted for measuring team performance, and two measures were used. The first dependent variable, rank_diff, measures the difference between a team’s actual league position and its expected rank, which is based on the team’s total market value at the start of the season, assuming that higher-value teams were expected to finish higher in the standings.⁹ For example, if a team with the fifth highest squad value finishes eighth, its rank_diff is −3. The second variable, point_diff, captures the difference between a team’s actual points and the average points historically achieved by teams with the same expected rank. For instance, if a team with the 5th highest market value earns 55 points in a season, but teams with a 5th-place expected rank historically average 60 points, its point_diff is −5.

Moreover, to isolate the impact of injury burden, additional control variables were included to account for other influential factors on team performance. Specifically, the models control the number of player signings and departures before each season, as these are known to influence team performance.⁹ Furthermore, dummy variables were incorporated to account for changes in head coach, either before or during the season.^{10 11} This approach aligns with the methodologies used in previous studies, such as those of Hägglund et al ⁸ and Lu et al,⁶ which also controlled for similar variables to ensure a more accurate estimation of injury-related effects.

Patient and public involvement

This study did not involve patient or public participation in the research’s design, recruitment or conduct. The analysis relied exclusively on anonymised, secondary data from statutory accident insurance records and publicly available team performance metrics.

Empirical approach

We employed team fixed effects panel regression models to analyse the association between injury rate and team performance. This approach exploits the longitudinal structure of the dataset, which includes repeated observations of each team over seven seasons (2014/2015 to 2020/2021). By incorporating team fixed effects ( ${\alpha }_i)$ , we control for all time-invariant team characteristics that might otherwise confound the relationship between injury rate and performance. In addition to the control variables ( $X_{it})$ , season fixed effects ( ${\sigma }_t)$ were included to account for temporal dynamics. The empirical models can be written as:

$${Team performance}_{it}=\beta {Injuryrate}_{it}+\gamma X_{it}+{\alpha }_i+{\sigma }_t+{\epsilon }_{it}$$

All injury measures were modelled as continuous variables and analysed separately. By focusing on within-team variation over time, this model allows us to estimate how changes in injury levels relate to team performance, independent of stable team characteristics. This fixed effects strategy enhances the internal validity of our estimates by isolating the effects of injury changes from unobserved heterogeneity across teams and improving robustness compared with random effects or pooled models. All SEs were clustered at the team level. Throughout the analysis, separate models were estimated for each injury variable across the two performance measures. The sample was split by division in all models to account for potential differences driven by division-specific characteristics and composition.

Monetisation

In the monetisation process, financial values were assigned to changes in league rank based on regression results. The regression model-generated coefficients were then matched to the corresponding financial differences between league positions. Revenue estimates were derived from two primary sources: TV broadcasting rights and prize money from international competitions, such as the UEFA Champions League and Europa League. The revenue data for domestic TV broadcasting rights were obtained from the website fernsehgelder.de. National and international distributions were considered, focusing on the equal payment each team received in the first and second divisions. Information on prize money was obtained from official UEFA financial reports.¹² For teams qualifying for international competitions, estimates were based on the average performance of Bundesliga clubs in the UEFA Champions League and Europa League over the observed period. The financial values for both categories were based on data from the 2021/22 season, while performance measures were averaged over the period 2014/15–2020/2021. This approach allows us to estimate the injury–performance relationship using multiple seasons of data while applying the most recent revenue figures to reflect the current financial context of professional football.

Figure 1 illustrates the expected financial outcomes for different league ranks in German football’s first and second divisions. For instance, missing out on Champions League qualification results in an estimated loss of approximately €27.0 million, while failure to qualify for the Europa League leads to a €5.6 million loss. Relegation from the first to the second division results in a financial hit of €20.8 million, primarily due to reductions in TV revenue. Based on the slope of the revenue curves, the average loss per one-rank drop across the table is approximately €4.9 million in the first division and €1.2 million in the second.

Figure 1.

Revenue distribution by rank in the first and second division of German football.

Results

Descriptive statistics

In the first division, the rank_diff variable’s SD of 3.58 indicates variability in team performance, while the points_diff variable’s SD of 8.18 reflects fluctuations in points. The second division shows even greater variability, with rank_diff at 4.86 and points_diff at 8.40, highlighting higher deviations from expected performance compared with the first division (table 1). Regarding injury measures, in the first division, teams lost an average of 753.67 days to injuries (SD: 367.39). In contrast, in the second division, the average number of days lost due to injuries was higher at 869.13 (SD: 394.39). The variable injury incidence indicates that teams in the first division averaged 21.36 injured players (SD: 3.98). In the second division, teams averaged 21.57 injured players (SD: 4.26). Finally, the results show that the first-division teams averaged 1.84 injured players on matchday (SD: 0.93), while the second-division teams had a higher average of 2.24 injured players on matchday (SD: 1.02).

Table 1.

Descriptive statistics by division

	First division (n=126)		Second division (n=126)
	Mean	SD	Mean	SD
Rank difference	---	3.58	---	4.85
Points difference	---	8.18	---	8.40
Injury incidence	21.57	4.26	21.35	3.98
Injury burden	869.13	394.39	753.67	367.39
Match unavailability	2.24	1.02	1.84	0.93
No. new signings	9.87	2.93	10.52	3.45
No. departures	8.34	2.94	8.98	3.23
Preseason coach change	0.37	0.49	0.44	0.49
In-season coach change	0.24	0.43	0.27	0.45

Regression results

Tables 2 and 3 present the results of the fixed effects models for team performance outcomes in both the first and second divisions (full set of regression models is in online supplemental tables 1 and 2). Across both leagues, the analysis mostly confirms a significant negative relationship between the injury measures and the difference between actual and expected team ranking. Injury incidence has a statistically significant negative impact in both divisions. In the first division, the coefficient of −0.231 indicates that an increase of 4.33 injured players is associated with a one-rank drop. In the second division, the impact is even larger: the coefficient of −0.379 suggests that only 2.64 additional injured players are needed to result in a one-rank decline. For injury burden, the estimated effect is negative in both divisions but only statistically significant in the second division. The coefficient of −0.003 indicates that 333 additional injury days are associated with a one-rank drop in the league standings.

Table 2.

Regressions results: rank difference

	First division		Second division
	B	95% CI	B	95% CI
Injury incidence	−0.231***	(−0.394 to −0.067)	−0.379***	(−0.658 to −0.099)
Injury burden	−0.001	(−0.003 to 0.001)	−0.003*	(−0.006 to −0.000)
Match unavailability	−0.616**	(−1.170 to −0.062)	−1.418***	(−2.404 to −0.432)
Observations	126	126	126	126

95% CIs in brackets.

*p<0.1, **p<0.05, ***p<0.01.

Table 3.

Regressions results: points difference

	First division		Second division
	B	95% CI	B	95% CI
Injury incidence	−0.690***	(−1.063 to −0.316)	−0.659**	(−1.150 to −0.168)
Injury burden	−0.005**	(−0.009 to −0.001)	−0.005	(−0.009 to 0.000)
Match unavailability	−2.355**	(−3.992 to −0.718)	−2.202**	(−3.907 to 0.496)
Observations	126	126	126	126

95% CIs in brackets.

**p<0.05, ***p<0.01.

Matchday unavailability shows a consistently significant negative effect. In the first division, the coefficient of −0.616 means that 1.62 additional unavailable players per matchday lead to a one-rank drop. In the second division, the coefficient of −1.418 suggests that just 0.71 unavailable players can cause a one-rank drop in performance.

Turning to the second performance measure, the difference between actual and expected points, the results show a consistently negative association between all injury measures and team performance across both divisions, with most effects reaching statistical significance. For injury incidence, the significant coefficient is −0.690 in the first division and −0.659 in the second division, meaning that 1.45 injured players in the first division or 1.52 in the second are associated with a 1-point loss. For injury burden, the coefficient is −0.005 in both divisions, indicating that 200 additional days lost due to injuries are associated with a 1-point loss. However, this effect is statistically significant only in the first division. Finally, for matchday unavailability, the first division shows a significant coefficient of −2.355, implying that 0.42 unavailable players lead to a 1-point loss. In the second division, the significant coefficient is −2.202, indicating a 1-point drop is associated with 0.45 unavailable players per match.

Monetisation

Based on regression results, injury incidence is associated with a one-rank drop for every 4.33 injured players in the first division and 2.64 in the second. Given an average injury incidence of 21.35 players in the first division and 21.57 in the second, this corresponds to rank losses of approximately 4.93 and 8.17, respectively. Applying the average revenue loss per rank of €4.9 million in the first division and €1.2 million in the second division, this results in estimated financial losses of €24.2 million and €9.8 million per season. Only the second division shows a significant association for injury burden, with 333 injury days corresponding to a one-rank drop. An average of 869.13 days lost implies a 2.61-rank change and an estimated €3.1 million loss. Matchday unavailability is significantly linked to performance in both divisions. In the first division, 1.62 unavailable players per matchday are associated with a one-rank drop; an average of 1.84 unavailable players implies a 1.14-rank decline and €5.6 million in lost revenue. An average of 2.24 unavailable players in the second division corresponds to a 3.15-rank drop, resulting in an estimated €3.8 million loss.

Discussion

This study examined the financial impact of player injuries on team performance over seven seasons in German football’s first and second divisions. It focused on the indirect costs of reduced performance and revenue losses due to player absences. Consistent with previous research,^{3 8} the findings reveal a significant negative relationship between the first and second divisions for most injury and outcome measures.

The financial implications of these performance declines are substantial. Depending on the injury measure, the estimated financial losses range from €5.6 million to €24.2 million in the first division and from €3.1 million to €9.8 million in the second division. According to these results, teams suffer the most financially due to the number of player injuries compared with injury burden or matchday unavailability.

Compared with prior studies that have monetised the impact of injuries using player salaries,^{5 6} the approach adopted in this paper provides a direct estimation of financial losses related to team performance declines. A comparison with the findings of Eliakim et al ⁷ reveals a smaller effect of injury burden. While Eliakim et al ⁷ estimate that 271 injury days correspond to a one-rank drop, the current analysis finds a comparable effect only in the German second division at 333 days, with no significant effect in the first division. However, direct comparison between the two studies is challenging due to data sources and context differences. Eliakim et al ⁷ rely on publicly available injury reports, while this study uses data from statutory accident insurance, capturing all officially reported injuries. In addition, the league structures and the financial stakes of specific league positions differ, which may also contribute to the variation in estimated effects.

When comparing effect sizes between the first and second divisions, the results show that injuries have a larger impact on points difference in the first division and a stronger effect on rank difference in the second. This aligns with Goggins et al,¹³ who found significant injury effects on team points only in the top tier of professional cricket. The differing impact across divisions likely reflects differences in competitive balance. Small point changes in the highly competitive first division can have major implications, such as qualifying for European competitions or avoiding relegation. In contrast, the more balanced second division allows injuries to shift relative rankings more easily. This is supported by the descriptive statistics, which show greater variation in final rankings in the second division.

Research/policy implications

This study provides new evidence on the financial consequences of injuries in professional football. Using a fixed effects model on a seven-season panel and detailed data covering multiple injury measures, the analysis isolates the impact of within-team changes in injury rates, yielding more robust estimates than prior cross-sectional approaches. These findings not only advance academic understanding but also have important practical implications. The substantial indirect costs associated with player injuries highlight the economic value of investing in effective injury prevention and management strategies. By reducing injury incidence and severity, teams can improve player availability, enhance performance and mitigate financial losses, especially in promotion and relegation systems where small performance differences carry financial implications.

Limitations

Despite its strengths, the study has several limitations. First, although key factors influencing team performance were controlled for, unobserved variables (eg, team cohesion) may still influence the results. Second, the financial estimations are based on average revenues and do not reflect the full complexity of other income streams (eg, sponsorships) likely linked to team performance. Therefore, the reported figures likely represent a conservative lower bound of the true financial impact. Future research could address these limitations by incorporating more detailed injury data (eg, player roles), evaluating the cost-effectiveness of injury prevention programmes, and extending the analysis to other leagues and sports to assess generalisability.

Ethics statements

Patient consent for publication

Not applicable.

Ethics approval

This study did not require ethics approval because it used secondary, anonymised data that were publicly available or collected from administrative records. The data included aggregated injury statistics and team performance metrics without any personally identifiable information about individuals.