2015 Sports Rehab Expert Teleseminar

One of the things that I get really excited about at the start of every year is the Sports Rehab Expert Teleseminar.

Joe Heiler always does a fantastic job getting some of the top rehab professionals from around the world and this year is no different. This year he has an awesome line-up (one speaker interviewed per week) of people like:

  • Charlie Weingroff
  • Donald Chu
  • Derek Hansen
  • Rob Panariello
  • Gary Gray
  • and several others…

For full details about the event and sign up information (it’s FREE!) check out THIS PAGE.

Happy learning!

Comparing Barry Sanders and Emmitt Smith

Barry Sanders and Emmitt Smith are arguably two of the greatest running backs of all time. They dominated the 90s with their running styles and were both eventually enshrined in the NFL Hall of Fame. Fans and media folk have often debated “Who was better as a running back”. It is a bit of a tough question to answer. First, stating the obvious, Emmitt played an extra 5 years than Barry. Emmitt played 15 seasons while Barry retired after just ten seasons, most people feeling he cut his career short. Secondly, Emmitt was on some exceptional Cowboys teams and had a supporting cast of great offensive linemen, a solid blocking fullback, and a hall of fame quarterback with great receivers, which set him up for opportunities that weren’t always present for Barry (the Lions only finished first in the, what was then referred to as the NFC Central, twice during Barry’s ten year career).

But what if Barry had played the extra 5 years that Emmitt did? Barry ended his career with 15,269 yards to Emmitt’s 18,355 career yards. How many more could Barry have gotten?

Side Note: I removed receiving yards from this analysis as I only wanted to look at rushing yards. Barry had 2,921 career receiver yards while Emmitt had 3,224 career receiving yards, most of those coming in his first nine seasons with the Cowboys.

First, let’s look at some visualizations of the career both of these superstars had. We will look at their yards per season, their average yards per attempt each season, and their average yards per game each season.

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Looking at the graphs, it seems like Barry was a more consistent runner than Emmitt and, after the age of 27, Emmitt’s production began to dip a little bit, while Barry stayed relatively consistent. During Barry’s 10 year career, he averaged 4.98 +/- 0.6 yards per attempt,  99.9 +/- 14.2 yards per game, and 1,526.9 +/- 270.8 yards per season. Looking at the first 10 years of Emmitt’s career, he averaged 4.3 +/- .46 yards per attempt, 90.3 +/- 17.6 yards per game, and 1,396.3 +/- 267 yards per season.

In the last 5 years of his career, Emmit averaged 3.62 +/- 0.5 yards per attempt, 59.42 +/- 19.9 yards per game, and 878.4 +/- 362.6 yards per season. It is important to remember that Emmitt missed six games during the 2003 season due to a fractured shoulder blade, causing him to only get 90 attempts that year (a far cry from his average of 269 attempts per year that he got during that time period, after removing the 2003 season from the data set).

Equivalence Coefficient

So, how much better would Barry have been had he played the same amount of games that Emmitt played – what amounts to 73 extra career games (since Emmitt missed 6 games in 2003, 2 in 2001, and 1 in 2004)?

In baseball, Sabermetricians will sometimes use the Equivalence Coefficient (EC) as a means of projecting out performance in specific metrics given an equivalent scenario.

First, we determine that, had Barry been given 73 extra games in his career, he would have had approximately 1461 total attempts (based off his previous career numbers). This allows us to calculate Barry’s EC:

1+(1461/3061)*1.00 = 1.477295

By multiplying this coefficient by Barry’s career total yards, 15,269 yards, we project that, given 73 extra games (or 1461 extra attempts) Barry would have rushed for a career total of 22,557 yards (4202 yards more than Emmitt).

Of course this assumes that during these extra 73 games Barry is 100% of the player he was in the past. Oftentimes, people will talk about the the magic age of 27 representing a “cliff” for running backs where their performance begins to drastically decline (it seems like Emmitt may have started to decline around this age). Looking at Barry’s charts above, it certainly doesn’t look like he was showing signs of decreased performance at the age of 27 and there is no reason to believe that he wasn’t going to be the same player in those next 73 games that he was in the previous 153 games. However, let’s assume that perhaps Barry is not the same player in those next 73 games. Looking at Emmitt’s production in his final 4 seasons (I took out the injury plagued 2003 season where he only had 90 attempts in 10 games), Emmitt saw a 16% decrease in average yards per season, a 6% decrease in average yards per attempt, and a 15% decrease in average yards per game. So, let’s say, hypothetically, that during the next 73 games, Barry would be 10% less of the player that he was in the first 10 years of his career (a 10% decline in his overall performance). If this is the case, Barry’s new EC is calculated to be 1.429566 and his projected total for career yards would have been 21,828. That is 3,473 more yards than Emmitt Smith had in his career.

What does it all mean?

It is fun to play with projections like this. This doesn’t necessarily mean that Barry was a better running back (remember, I didn’t factor in receiving yards) but it certainly does show the incredible skills that Barry had and what he was able to accomplish in a career that he ended too soon (in the opinion of many). From a sports science/strength & conditioning perspective, projecting out performance allows us to understand the potential that our athletes have and what their projected performance may look like given a decrease in their overall output. This is important in a team sport as the chaotic nature of performance is difficult to tie back to the S&C coach (it certainly is not as easy to make the connection as it is with an individual athlete sport, such as track and field, swimming, or cycling). With this information we can begin to grasp the value that our programs may have on prolonging an athlete’s career by keeping them healthy and on the field/court where they can perform at the highest level, helping them to maintain their performance without seeing as large a decline with age as other athletes may see.

Massage & Muscle Stiffness

One of the main reasons that athletes seek out massage is to decrease the muscle stiffness they feel due to intense training and competing. A recent paper by Crommert and colleagues (Scand J Med Sci Sport, 2014) evaluated the effects of massage on muscle stiffness in the medial gastrocnemius of eighteen healthy volunteers.

Methods

Seven minutes of massage was performed on the gastrocnemius of one leg for each subject – 2min of effleurage, 2min of petrissage, 2min of deep circular friction, and 1min of effluerage – while the non-massaged lower leg served as the control. Immediately following massage, the subjects rated their level of pain experienced during the massage on a 0 to 10 scale (0 = no pain, 10 = worst imaginable pain).

Muscle stiffness was measured using shear wave elastography to quantify the shear elastic modulus (stiffness) at the midpoint of the medial gastrocnemius muscle belly at three time points: before massage (baseline), immediately following massage (follow-up 1), and after 3min of rest following follow-up 1 (follow-2), in both the massaged and non-massaged legs.

Findings

  • Medial gastrocnemius stiffness was significantly lower immediately following massage (follow-up 1) compared with baseline and following rest (follow-up 2).
  • There were no significant differences found between baseline and follow-up 2 in the massaged leg, indicating a return to normal muscle stiffness.
  • Average level of pain rating was 1.3 +/- 1.6 and there was no correlation found between perceived pain level and a reduction in muscle stiffness in the massaged leg at follow-up 1.

Conclusion

Massage appears to reduce muscle stiffness; however the results are short lived with a rapid return back to baseline levels.

Practical Applications

The authors suggested four potential mechanisms that may lead to a decrease in muscle stiffness from massage:

  1. A decrease in motoneuron excitability due to general relaxation.
  2. Manual pressure and stretching leading to a breaking apart of stable cross-bridges between actin and myosin filaments, which are spontaneously formed while the muscle is at rest.
  3. Increased intramuscular temperature from the massage.
  4. The possibility that all of these mechanisms are working together, rather than any one of them working in isolation.

These theoretical mechanisms for why manual/touch therapy works are interesting and most likely not the only mechanisms at play. I’d be inclined to think that #4 above is the most likely scenario, along with other potential influences.

The fact that massages influence on muscle stiffness was short lived is interesting. From a practical standpoint, when applying this stuff to athletes for specific purposes of addressing muscle tone and stiffness, there are a few things I think about with regard to the outcome in this study:

  • The length of treatment may have been too short to produce a more longer lasting effect. Maybe seven minutes isn’t enough? One proposed mechanisms that led to a decrease in muscle stiffness was general relaxation from massage. While not measuring stiffness, Arroyo-Morales have done some studies looking at massage therapy and autonomic changes – a shift towards a more parasympathetic state – leading to greater relaxation. The two studies they performed used 40min massages following intense cycling exercise in order to achieve this result.
  • Maybe the techniques used are to passive in order to produce longer lasting changes? As I discussed a few weeks ago, there might be different massage techniques for different recovery purposes. If the goal is to improve some sort of functional outcome (E.g., decrease muscle stiffness and/or improved ROM) maybe passive techniques, like the ones used in this study, need to be coupled with more active techniques which force the client to be an active participant in the treatment. This puts the client in the driver seat and might allow their brain to be more receptive to the changes taking place and cause them to be more longer lasting.
  • Finally, maybe the treatment needed to be followed up with active movement in order to “make it stick”? In the past, I have written about the idea that massage might be useful to “open the window”, to help decrease threat or increase awareness for the client, and then should be followed up by movement therapies in order to teach the brain to move and be strong through the new ROM on its own. Perhaps the reduction in muscle stiffness, found in this paper, would have been longer lasting with movement therapy? Certainly a short treatment time can be beneficial in certain situations, depending on your goal. Grieve and colleagues found that a 10min treatment consisting of trigger point therapy and light stretching was adequate enough to produce a significant increase in ankle dorsiflexion in recreational runners. In a situation where the goal of treatment is some sort of functional outcome, rather than more recovery based, these short bouts of massage therapy may be enough to produce a result and then should be followed up with some sort of movement based therapy.

Massage therapy appears to impact the body on different levels via different mechanisms. This study evaluated muscle stiffness and found that seven minutes of massage was effective at decreasing muscle stiffness, however, the results were short lived. From a practical standpoint, the fact that massage decreased muscle stiffness is promising and there might be other factors that could enhance the effect of the positive change in muscle stiffness seen in this study. In an actual treatment setting we rarely (or never) rely solely on one single modality or approach and usually a variety of different approaches are stacked on top of each other, depending on the intended goal of the treatment. When used in conjunction with other modalities, the findings from this study may potentially be augmented.

NBA Super Teams – “They Just Need to Learn To Play Together”

Being a life long Cavs fan, I was excited to see LeBron return to Cleveland to try and make a run at a championship. I’ll admit, in 2010, when he made the decision to leave for Miami, I was pretty upset; but, I don’t fault players for going to different teams if it means more money and better opportunities to win championships. Additionally, how can you fault a guy for wanting to play on a super team with athletes like Dwyane Wade and Chris Bosh? What makes LeBron’s second stint in Cleveland so exciting is the possibility of another super team, this time with Kevin Love and Kyrie Irving. Of course, when these teams get together and don’t automatically win 20 straight right out of the gate, fans tend to get a bit unruly and start to jump ship.

Similar to LeBron’s first year in Miami, it took some time for things to click between the players on the team. The media pundits always like to bring it back to the fact that the players need to, “Learn to play together”,  because, as some point out, they have such similar games and are all three such dominant players on the court that they have to figure out who is going to play which role (which might actually change from game to game).

With that in mind, I decided to look at some of the data of these players on the super teams to see how similar they are and, perhaps, try and understand how similar LeBron’s current super team, the Cavs, is to his former super team, the Heat.

The Data

Since I don’t have access to a ton of NBA data, I took whatever I could get a hold of from www.basketball-reference.com. I compiled the player data for all of the years LeBron was at the Miami Heat, all of the years Kevin Love was at the Minnesota Timberwolves (his entire career), and all of the years Kyrie Irving has been at the Cleveland Cavs (his entire career).

Only players who participated in more than 30 games per season where included (NOTE: Doing this removes Kevin Loves 2012-2013, as he only played in 18 games that season do to several hand injuries).

I then created a cluster analysis to evaluate how similar or dissimilar players in the data set were (you can click on the picture to make it larger).

Screen Shot 2014-12-07 at 5.14.50 PM As we see, LeBron lies on a node all to himself, and rightfully so! LeBron is a truly unique player, who can play every position on the court and is both a significant defensive and offensive threat. To the right of Lebron, we see a second node, which then breaks down into two more nodes. It is in this cluster on the tree that we see the main players we care about – those that make up LeBron’s former and current super team. Kevin Love and Chris Bosh are clustered close together while Dwyane Wade and Kyrie Irving are clustered close together, owing to the similarities in their game.

LeBron’s Supporting Cast

We see that the supporting cast for LeBron have some similarities when we evaluate their performance metrics within the data set of these three teams (Cavs, Heat, and Timberwolves). Looking deeper at LeBron’s supporting cast in both Miami and Cleveland allows us to see how each of these players compare to each other.

Screen Shot 2014-12-07 at 6.07.04 PMLooking at Field Goal Percentage, we see that LeBron’s former teammates are a clear favorite when it comes to making shots. However, it is important to remember that this data is showing us the years that Wade and Bosh also played with LeBron. Perhaps having LeBron on the team created new opportunities for Wade and Bosh to score, opportunities that Irving and Love did not have given the weaker teams they were on?

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When it comes to rebounds we see that Kevin Love has had more success in this category than the other four players, while we see that Bosh is roughly equal with James.

Below is some comparison of the four players against each other for a few of the other metrics in the data set (all graphs are showing mean +/- SD).

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Conclusions

It is interesting to compare these players against each other. I am a bit surprised to see Bosh with such a low number of assists compared to the rest of the group. Additionally, it is interesting to see how many blocks Wade and Bosh had in comparison to Irving and Love. The Heat were definitely a defensive juggernaut, while also being a huge offensive threat, making them one of the most dangerous teams in the NBA. While it appears, from the cluster analysis, that Love and Irving play similar games to Bosh and Wade, it seems like Bosh and Wade are slightly better in some of the statistical categories.

Wade was already a great player (one of the best ever) before LeBron, and Bosh was no slouch himself. I wonder if “figuring out how to play together” allowed them to improve different aspects of their game, as having a unique player like LeBron on the court tends to draw a lot of interest from the opponents defense, opening up new opportunities for Wade and Bosh. One way to evaluate that might be to look at their individual statistics during their career before LeBron and then after LeBron. Additionally, it might be interesting to compare how much LeBron’s game changed from his first years with the Cavs and then his years with the Heat, where he “figured out how to play” with the other superstars on the court.

It will be interesting watch James, Love, and Irving, figure it out over the next few seasons. They have potential to turn this into another big three and hopefully dominate the NBA. With the similarities between Bosh and Love, and Wade and Irving, perhaps LeBron can put his new teammates into a good position to elevate their game and take things to the next level.

Minimum Effective Training Dose

You hear the phrase all the time, “I’m a real minimum effective dose guy”, or, “We train only as much as we need and then no more”.

Everyone says these things, but what do they really mean? What is a “minimum effective dose”? Is the minimum effective dose different for different people? Do some people need more training and some less? While the phrases sound good on paper or when uttered at a training conference, how do we take the theory of the minimum effective dose and turn it into practice?

To be fair, these are great ideas and statements that really do resonate with me in my approach to program design. Why expend physical resources (energy) on training that are unnecessary and potentially limiting your recovery from the previous session, thus diminishing your ability to train harder the next time around? As I like to say, “There is always a cost of doing business. All training comes at a cost and in order to reap the benefits you need to make sure you pay that cost and then replenish the checking account before paying again.”

Recently, I had a great discussion with two colleagues I respect – Sam Leahey and Nate Brookreson. We were discussing concepts around an individualized training approach, and the main discussion points began with us first reading and talking over two papers by Kiviniemi, et al., Endurance Training Guided Individually by Daily Heart Rate Variability Measurements (Eur J Appl Physiol, 2007) and Daily Exercise Prescription on the Basis of HR Variability Among Men and Women (Med Sci Sports Exer, 2010).

Both studies utilized a similar type of training approach for the two training groups. One group performed a standard, predetermined training program – just like a coach would write for an athlete, dictating what should be done each day of the week (exercises, load/intensity, sets, reps, etc). The other group performed their training based on their HRV readings taken first thing in the morning, upon waking. The mode of exercise in the studies was endurance training, and days were broken into high intensity (40min at > 85% of maxHR) or low intensity (40min at 65-70% maxHR) or complete rest.

The way it worked for the HRV-dictated training group was that they would take their HRV, and based on the outcome, compared to a rolling average, they would alter their training for the day performing either a high intensity session, a low intensity session, or taking a rest day. Thus, training was guided by what the body was prepared to do.

Interestingly, the HRV-dictated training groups improved their fitness while training high intensity sessions less frequently during the study period than the predetermined training group (More is not better. Better is better). Basically, on days when their body was ready for a high intensity training session they went for it, and when their body was not ready they backed off and allowed the body time to replenish the checking out, so to speak, before repaying the cost. They gave the body what it needed.

Some of my thoughts

Heart Rate Variability is not the be-all-end-all of athlete monitoring, as some make it out to be. It is one small piece (a small piece with rather noisy data, mind you) in a much larger puzzle. That being said, I do believe it can have a role in athlete monitoring if you understand its limitations, standardize the collection process, and couple it with other methods of monitoring the athlete and evaluating their capability and capacity on a given day.

These studies seem to move us closer to understanding the concept of a minimal effective dose and perhaps offer a newer approach to program design and periodization – similar to the concept of auto-regulation. Earlier this year I put together a decision tree for training, similar to the one shown in one of the studies mentioned above, where a few factors were taken into consideration and put into the tree, and the results of those factors allowed the athlete to alter their training program based on the input they plugged in. This allowed us to adjust the program up or down on a given day based on how the athlete was responding. Instead of writing training programs that told the athlete to do “x” on Monday, “y” on Wednesday”, and “z” on Friday, the athlete was given different workouts with different training targets (2 workouts reflecting the main physiological targets of the training block, 1-2 workouts reflecting the secondary, or maintenance, physiological targets of the training block, and 1 recovery based work).

Depending on how the athlete was reporting that day, we would choose which workout to do. This would end up sometimes pushing our training week out longer than 7 days (sometimes it would take 10 days to get through the training cycle). This was apparent, particularly, in older athletes whose bodies took longer to recover from the training session or athletes who were out of shape and lacked fitness and needed the extra time to make appropriate adaptations to the training stimulus imposed upon them. If we were working on a timeline and had a set duration of time to perform a block of training (for example the athlete would only be able to train 10 weeks in an offseason), we would adjust the workout on a given day by lowering either training volume or training intensity (which of those we lowered was dependent on the physiological targets of that phase of training and what the main objective was).

What was also interesting in the studies above was that if the subject had recovered the following day from a high intensity training day they would then perform another high intensity session (although after two successive high intensity sessions they were asked to take a rest day). The high-low training concept of organizing high intensity stressors on one day and low intensity stressors on another day is a great one and one that I have used for many years; however, there are times when the athlete needs to be able to put together back-to-back days of high intensity work due to competition (i.e., basketball or hockey) or hard practices (i.e., NFL training camp) being stacked together. By using a training approach driven by monitoring the athlete’s response and adjusting the workout to suit their needs and abilities on a given day, we can slowly build up the athlete’s resilience to tolerate high intensity work to a level that allows them to train hard, recover quickly, and then train hard again. This is a key piece that ties together the stress resistance/stress tolerance and fitness components of my Physiological Buffer Zone methodology, which I discussed in THIS interview.

What it basically boils down to is that each athlete is an individual. Each athlete has a different way of responding and adapting to the training stress you apply to them (and even to the treatment stress if you are using soft tissue work!). The time it takes to recover and make favorable adaptations to a training session may differ from one athlete to the next, and an individualized approach, based on monitoring various qualities, is essential to understanding what the athlete needs. Too often coaches try and force fit an athlete into their training program without respecting these laws of individualization. Hopefully the future will allow for better methods to test athletes, monitor/evaluate athletes, and adjust training for athletes to ensure that their body receives the type of training it needs – the correct amount at the correct time.