Monitoring, Injury Risk Factors and the 21st Century Strength Coach - By Eamonn Flanagan


Eamonn Flanagan


“To bankrupt a fool, give him information”

Nassim Taleb


The 21st century strength coach operates in the world of big data. On a daily basis we are exposed to countless data collection, data analysis and intervention opportunities.

Take even simplest of training load monitoring systems – the session RPE system. With this method we log training time (minutes) and training intensity (1-10 scale) yet from this ever so basic tool we generate multiple metrics: daily and weekly training loads, training monotony and strain, training stress balance and percentage breakdown of training load per activity. We have big data before we even consider more highly involved and detailed datasets from neuromuscular fatigue tests, heart rate variability and GPS monitoring systems.

While each of these monitoring systems can have merit, this exponential increase in data creates problems for coaches to filter signal from noise. Information overload can blind us to the more obvious issues right in front of us. We are humans first and coaches second and the human brain did not evolve to process such large amounts of data. Our eyes look for patterns and are often biased to see trends where none may exist. This is a particular challenge in team sports where there are large playing roster numbers, a chaotic environment and short time periods to assess and action our data. How do we quickly drill down to the data that is most meaningful? Who are the priority athletes? What data should be actionable and when?

S&C coaches and sport scientists needs to develop a “monitoring filter” to protect us from becoming the fool who is bankrupted by decision making in the face of big data. Looking at daily monitoring systems and making interventions without viewing the wider context and the athlete’s history is a case of S&C coaches being guilty of a “ready-shoot-aim” philosophy of athlete management.

Ultimately, the aim of workload monitoring systems is to optimize the training process for athletes and to protect them from unintentional overload and elevated injury risk. But some authors have highlighted that the relationship between workload and injury risk is not as straightforward as might wish to think1.


“The degree of individual variation will be considerable; it is very difficult to determine the relationship for each individual, and without doing so it is unsafe to generalize across individuals”

Dr. Paul Gamble


So before we even glance at our big data monitoring tools we should have a clear outline of which of our athletes may be most at risk. For some, the “red flags” from monitoring systems may be far more meaningful than others.

Here I identify 6 key injury risk factors which should be considered for team sports athletes and I outline how we can review them within the multi-disciplinary team to prioritize the most “at-risk” athletes. An athlete with a number of these risk factors may merit much greater management and intervention in light of monitoring system “red flags”.

Figure 1: Injury risk factors in team sports

Figure 1: Injury risk factors in team sports

Previous injury history is likely the strongest predictor of future injury for athletes. Icelandic researchers tracked the injury information for 20 teams and 306 players across a full competitive football (soccer) season2. They clearly showed that a history of prior injury significantly increased the likelihood of the same injury within the season. This relationship was particularly clear for hamstring injuries, groin strains and ankle sprains.

This was also demonstrated in UEFA’s comprehensive injury surveillance research study led by Sports Medicine Professor Jan Ekstrand. This study studied 26 teams and 1400 players over 10 years. Players with a muscle injury in the preceding season had up to three-fold increased injury rates compared to previously uninjured players – this was true for hamstring injuries, groin injuries and knee joint traumas3.

Figure 2: Previous injury history is a strong predictor of future injury. Data adapted from Arnason et al. 2004)

Figure 2: Previous injury history is a strong predictor of future injury. Data adapted from Arnason et al. 2004)


Concussion and its effects in sport is a hot-topic right now and with good reason. As our understanding of this area grows, a worrisome picture emerges. While we are developing a greater understanding of the effects of concussion on cognitive function and mental health, it appears that suffering a concussion can also have a significant effect on an athlete’s likelihood of suffering an additional injury… of any type. The rugby science research group in Bath University showed that professional rugby players who suffered a concussion had a 60% greater risk of picking up a further injury in the remainder of the season than players without concussion4. Similar findings have been reported in soccer with players who suffered a concussion 50% more likely to get injured that season5. These odds seem to stack up against the concussed player particularly in the first 90-days post-concussion. An investigation into NCAA Division 1 athletes showed that the odds of suffering a lower body injury were raised by 150% in concussed athletes versus matched controls who had no concussive history in this 90-day acute period6. The research and evidence is still developing in this area but practitioners wanting to maintain a strong duty of care to their athletes should review concussive incidents as part of their athlete reviews.


Age is a may also be a predictor of general injury risk and older players typically suffer a higher frequency of injury in team sports1. Dr. Tim Gabbett and his research team have presented data highlighting that Australian Rules footballers with greater training experience (7+ years) have higher probabilities of injury when exposed to large fluctuations in training load7 and Dr. Gabbett has suggested that older players may need to be “managed” through the latter years of their playing career to maximize their time on the playing pitch8.

An additional key finding of the 10-year UEFA study, was that age, along with previous injury history, was a clear factor that caused increased injury risk in football players. The Icelandic football study also reinforced this “age effect”. Athletes who suffered injury were significantly older than those who were injury free throughout the season. Players in the oldest age group (29-38 years) had the highest injury incidence. Age also appeared to have a particularly strong impact on hamstring injuries - players who suffered hamstring injuries were typically 4 years older than those who did not.

Strength & Conditioning

Strength & Conditioning of appropriate levels are intuitively considered by most S&C coaches to protect against injury – but the empirical evidence supports this view. Strength has a significant protective effect against joint and muscle injuries. Stronger muscles are better equipped to stabilize and protect joints against high-risk external forces. Dr. Stuart McGill, the legendary professor of spine biomechanics, describes injury thusly:

“Injury, or failure of a tissue, occurs when the applied load exceeds the failure tolerance or strength of the tissue.”9

Strength training, is the simplest way to increase tissue strength and improve the failure tolerance of muscle, tendon and ligaments. High strength levels protect against acute injury.

Figure 3: The injury mechanism. Applied load vs the strength of the supporting tissues. (Figure adapted from McGill, 1997)

Figure 3: The injury mechanism. Applied load vs the strength of the supporting tissues. (Figure adapted from McGill, 1997)

The link between strength and reduced injury risk has been shown as both a general and specific effect. A comprehensive review in the British Journal of Sports Medicine examined the effect that different types of training have on the incidence of injury10. Strength training, stretching and proprioceptive training interventions were all included. The researchers analysed the results of 25 studies involving over 25,000 athletes with over 3000 incidents of injury. Across studies, general strength training provided a “highly significant” protective effect against injury and “reduced sports injuries to less than one-third”.

In an in-depth study from Rugby League in Australia, stronger athletes (as measured via a 3RM back squat) accrued less muscle damage compared with weaker players despite accumulating greater workloads in competitive games11. The better conditioned athletes in this study (measured with a yo-yo aerobic test) were also shown to recover quicker from the demands of games. As such, stronger, fitter players are likely to carry less fatigue into consecutive games and better able to tolerate busy playing schedules. Again, this highlights the general protective effect of strength on injury risk reduction.

Specific strength qualities also play a role. Australian researcher David Opar of the “Hamstring Injury Research Group” investigated the effect of specific hamstring strength on the potential for hamstring injury in over 200 elite Australian footballers12. The research demonstrated that low levels of eccentric hamstring strength increase the risk of potential hamstring injury. A number of other more specific strength-injury relationships exist in the scientific literature, for example neck strengthening programs are associated with lower risk of neck injuries and concussive injuries. External rotator strength training may decrease the likelihood of injury in “throwing dominant” or “overhead activity” athletes.

Changes in body weight and body composition can have also have a meaningful effect on the likelihood of specific injury types. A weight gain of 1kg results in a 4kg increase in compressive force at the knee in walking13 and has an even greater effect in more athletic activities like sprinting and jumping. Any such weight gain must be accompanied by significant increases in strength and stability to protect against these increases in knee loading. Any negative changes in body composition – more fat mass or less muscle mass – may expose athletes to increased injury risk to chronic knee conditions.

Risk factors amplify each other. For example, overweight high-school athletes with previous ankle injuries have been shown be 19 times more likely to incur a non-contact ankle injury than an athlete with no injury history who is at an appropriate bodyweight14. A further finding of Opar’s hamstring injury research has been that high levels of eccentric hamstring strength can attenuate the potential risk factors of age or previous hamstring injury – the elimination of one risk factor can help dampen the effect of another.

Fiber-typing and the explosive profile

So while in most of the risk factor examples above, I have tried to make a strong scientific case for each. But in this next example, I can’t – the published evidence is very limited. But I propose here that athletes with fast twitch fiber-type profiles, “explosive profile” athletes are more at risk to injury and need to be managed accordingly. This is an argument based on logic more than the scientific research. A google scholar search of “fiber-type” + “injury” returns zero studies that have actually explored this in detail but an absence of evidence is not evidence of absence. There is some research suggesting that fast twitch dominant athletes need longer tapering strategies to see the same increases in “freshness” or “readiness”. There is also evidence that fast twitch fibers may be more susceptible to eccentric muscle damage… but overall there isn’t a proven link here between fiber-type and injury risk. However, the logical argument is a strong one.

The explosive athlete is capable of phenomenally high “outputs” – they sprint faster, jump higher and hit harder than their slow-twitch peers. This makes them athletically gifted and valuable to the team but exposure to such high forces and impacts exposes them, more often, to high, injury-causing, external forces. Essentially they have a greater capacity to do themselves harm. The very factor which makes them talented also exposes them to potential risk.

The explosive athlete may also require longer recovery periods within the game and within the training week. Explosive athletes will deplete energy reserves quicker and may be exposed to a longer time course of acute metabolic recovery and more chronic central nervous system recovery. These effects compound to create a perfect storm for the explosive athlete. They have greater outputs, are exposed to higher forces and are more likely to do so in the face of underlying fatigue… all leading to elevated injury risk.

Again, I stress here that the scientific evidence linking fiber-type to injury risk directly is absent but my experience is that the most progressive and successful sport science and S&C programs in team sports are managing these talented players in an individualized fashion to ensure their regular availability for key training sessions and competitive action. Genetic testing is developing but ultimately jump test scores and speed times are an easy way to identify the fast-twitch freaks in your program.

Implementing risk factor profiling I am not suggesting that this an exhaustive list of risk factors – there are many other factors which could be considered such as underlying biomechanical issues, chronic gametime exposure and there may be sport specific risk factors also. However here I have focused on the simplest factors which any coach and multi-disciplinary team can review and assess. In my opinion, each of these risk factors are a much stronger indication of “risk” than the majority of commonly used monitoring tools.

Objective and subjective monitoring tools can provide insight into the “acute” situation the athlete is faced with. For example, these tools can provide a snapshot of the current effect of lifestyle and non-training stressors on athletes. This is key as these factors can alter athletes’ perception and response to a given workload. But the risk factors outlined thus far provide the “bigger picture” context of the current situation. It should also be noted here that the same athlete will demonstrate different responses at different time points. The stimulus which could be injurious at one time of the year, might not at another (Gamble – in conversation). So for each athlete, these risk factors must be reviewed, discussed and digested regularly in order to appreciate daily monitoring data in the appropriate context. Acting on monitoring data, without an appreciation for inherent risk factors like these is akin to a doctor ordering invasive, advanced screens for coronary heart disease before he has asked the patient if they smoke. Nor am I suggesting that these risk factors are new concepts for many S&C coaches. They are part of the subjective context we draw on in much of our decision making around our athletes. But in order to avoid blind spots and biases, an objective, regular systematic review of these factors makes a big difference. It also encourages multi-disciplinary team communication and “shared ownership” of injuries and injury-avoidance.

Below I have outlined a simple method that I have used in elite team sport to quickly assess these risk factors within the multi-disciplinary team. Ideally sport science, S&C, physiotherapy/medical and the sports coach collaborate on this review. Each player can be benchmarked against the objective categories and scored as high risk (3 points), moderate risk (2 points) or low risk (1 point). This quickly creates an objective ranking of a squad to highlight athletes who may require much greater program management and warrant much greater action in light of unfavorable monitoring data. This is a simple system, many high-level programs will have much more sophisticated review processes, but it can be a starting point for many.

Figure 5: An example of squad profiling for injury risk factors.

Figure 5: An example of squad profiling for injury risk factors.

Consider the athlete review data above. If our monitoring systems are showing a spike in training-stress balance or a decline in wellness markers of equal measure for athlete 11 and athlete 8 should we react in a similar fashion for both athletes? Athlete 11 is an older, more explosive player who has a significant injury history, has a history of concussion and has poor strength levels. Athlete 8 is young, strong and well-conditioned with minimal injury history. Athlete 11 needs to be managed accordingly while athlete 8 will be more likely to cope more effectively with these momentary periods of stress and increased workload. One of the main reasons we train strength and conditioning development is to promote robustness. Removing athletes from team training should be the last resort, so understanding which athletes are most likely to cope through tough times is key. This isn’t a foolproof system, it doesn’t prevent injury, but it may help us make slightly more effective decisions in the management of our athletes.

Once players are reviewed in such a fashion it quickly helps identify those “red flag” players whose monitoring data should be reviewed daily and who may warrant more aggressive training modifications. When we are short on time, in the chaotic team sport environment, it is these red flag players with whom we should check in most regularly. This process also helps identify the potentially most robust players in the group. In an era of necessary squad rotation these players may be the ones who can most readily cope with the demands of congested playing schedules. Modern day S&C coaches are challenged in an era of big data collection and analysis and we can be inherently biased or fooled by the randomness of complex datasets. Our understanding of the athlete must drive our interpretation of the data – the data cannot drive our understanding of the athlete. Establishing and understanding every athlete’s individual risk factors can help us frame monitoring data in a much stronger context and enable us to make more targeted and effective decisions on load management.


Gamble, P. Comprehensive strength and conditioning: physical preparation for sports performance. 2016.

Arnason, A., Sigurdsson, S.B., Gudmundsson, A., Holme, I., Engebretsen, L. and Bahr, R., 2004. Risk factors for injuries in football. The American journal of sports medicine, 32(1 suppl), pp.5S-16S.

Hägglund, M., Waldén, M. and Ekstrand, J., 2013. Risk factors for lower extremity muscle injury in professional soccer the UEFA injury study. The American journal of sports medicine, 41(2), pp.327-335.

Cross, M., Kemp, S., Smith, A., Trewartha, G. and Stokes, K., 2015. Professional Rugby Union players have a 60% greater risk of time loss injury after concussion: a 2-season prospective study of clinical outcomes. British journal of sports medicine, pp.bjsports-2015.

Nordström, A., Nordström, P. and Ekstrand, J., 2014. Sports-related concussion increases the risk of subsequent injury by about 50% in elite male football players. British journal of sports medicine, 48(19), pp.1447-1450.

Brooks, M.A., Peterson, K., Biese, K., Sanfilippo, J., Heiderscheit, B.C. and Bell, D.R., 2016. Concussion increases odds of sustaining a lower extremity musculoskeletal injury after return to play among collegiate athletes. The American journal of sports medicine, p.0363546515622387.

Rogalski, B., Dawson, B., Heasman, J. and Gabbett, T.J., 2013. Training and game loads and injury risk in elite Australian footballers. Journal of Science and Medicine in Sport, 16(6), pp.499-503.

Gabbett, T. Training smarter and harder. Seminar series hosted by the IRFU and Institute of Technology Tallaght, 2015.

McGill, S.M., 1997. The biomechanics of low back injury: implications on current practice in industry and the clinic. Journal of biomechanics, 30(5), pp.465-475.

Lauersen, J.B., Bertelsen, D.M. and Andersen, L.B., 2014. The effectiveness of exercise interventions to prevent sports injuries: a systematic review and meta-analysis of randomised controlled trials. British journal of sports medicine, 48(11), pp.871-877.

Johnston, R.D., Gabbett, T.J., Jenkins, D.G. and Hulin, B.T., 2015. Influence of physical qualities on post-match fatigue in rugby league players. Journal of Science and Medicine in Sport, 18(2), pp.209-213.

Opar, D.A., Williams, M., Timmins, R., Hickey, J., Duhig, S. and Shield, A., 2014. Eccentric hamstring strength and hamstring injury risk in Australian footballers. Medicine & Science in Sports & Exercise, 46.

Messier, S.P., Gutekunst, D.J., Davis, C. and DeVita, P., 2005. Weight loss reduces knee‐joint loads in overweight and obese older adults with knee osteoarthritis. Arthritis & Rheumatism, 52(7), pp.2026-2032.

Tyler, T.F., McHugh, M.P., Mirabella, M.R., Mullaney, M.J. and Nicholas, S.J., 2006. Risk factors for noncontact ankle sprains in high school football players the role of previous ankle sprains and body mass index. The American journal of sports medicine, 34(3), pp.471-475.