The Metric Hamburger
This has been the decade for measurement in sports science. With the advancement of technological equipment, we have become more objective than ever in assessing “performance.” Sleep tracking, GPS, internal load, force plates, readiness, and other relevant biomarkers place numbers on outputs and seek to create hierarchical norms. “Your sleep score today is 93.4,” sounds much more precise and accurate than simply asking an athlete “how did you sleep?” Is one better than the other? Perhaps this is debatable pending validity, buy-in, accountability, and content knowledge, but what is not, is that at the end of the day subjectivity matters. In fact, subjectivity may be more important than ever for today’s performance coach who is swimming in an ocean of noise begging to be thrown the preverbal signal/life vest.
I am not a metric nihilist. I measure. I manage. However, I do rely on my critical thinking skills. A number is simply a number. It needs context to breath, to narrate, to become actionable. That context comes in the form of assumptions and analyzation, both are subjective. I call this the metric hamburger.
On each side of the burger is a bun. The top and bottom buns are subjective, the hamburger is the “metric.”
Assumptions (Top Bun):
What’s the problem being solved?
Who chooses what to measure?
Face validity of the measure
Validity of the instrument used to measure
Is there a learning effect for the measure/metric?
Can I rely on my first principles knowledge (physiology, biomechanics, tactical, technical)?
Burger = Metric
The bottom bun is subjective:
Analyzation/Communication (Bottom Bun):
How do we interpret this number
What are the limitations to this metric
How do we communicate this to the athlete/coach/front office
In the best of times, we are subjective beings. We are driven by stories and emotions, not spreadsheets and box plots. Every metric has its limitations. Some, more useful than others, but we must critically think before engaging in measure in order to avoid the “Wizard of Oz” fallacy which has proven detrimental in the social sciences. In fact, it morphed into Campbell’s law which states: the more any quantitative social indicator is used for social decision making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.” This is not the future we want as performance professionals.
Sports performance lives in the world of complexity and uncertainty. Use metrics, use heuristics, but always foster your subjective reasoning…and don’t apologize for it!
The Eyelet Controversy: A Biomechanical Perspective
(This article was originally published in January, 2019).
Recently, there has been some fruitful dialogue by several close collogues regarding how best to lace up a pair of hockey skates for increased performance on the ice. The idea of leaving the first eyelet untied in hopes of producing greater speeds was reinforced in a December article titled “The NHL’s best young skaters all have something in common-how they tie their skates” in The Athletic. The purpose of this blog is to briefly outline the biomechanical considerations involved in this decision. Prior to moving forward, we must first define a hockey stride. According to Marino (1977) a hockey stride is:
“Biphasic in nature consisting of alternating periods of support (single leg and double leg support) and swing. The single support phase corresponds to a period of glide, while the double support phase corresponds to the onset and preparation of propulsion.”
So Why the Missing Eyelet?
The goal with purposefully skipping the eyelet from a biomechanical prospective is to gain increased range of motion, in particular at the knee and ankle joints. Basic biomechanics states that increase velocities result in increased joint amplitudes. (Lafontaine, 2007). In other words, in order to move fast, you better have the requisite mobility. According to Marino, the best technique to achieve acceleration is:
1. High stride rate
2. Increased forward lean at touch down
3. Short single support phase
4. Placement of recovery foot below hip
By purposefully leaving the first eyelet untied we directly affect bullet number two. Getting lower in the hockey stride reduces the angle of the torso in relation to the ice, thus reducing air friction.
Trunk Segmental Angle: Green acute angle; Shank: Red acute angle
Leaving the first eyelet untied enables the hockey player to get deeper in their position.
In addition, a lower knee bend directly affects stride length and propulsion time, both indicators of the amount of impulse being applied to the ice. (McPherson, Wrigley, & Montelpare, 2004). In addition, skaters create a propulsive angle between the skate blade and the ice surface in order to create the desired ground reaction force. This angle is relatively large at the start of run to glide in order to build sufficient friction. The potential to increase this angle due to greater ankle eversion, may be yet another performance byproduct of leaving the first eyelet untied.
The propulsive angle (theta) is greater during run to glide. Increase in mobility (eversion) may benefit increasing this angle, thus creating more friction to overcome inertia.
Step width is strongly related to stride length during run to glide as higher caliber players created wider widths and longer strides during run to glide.
Additional Thoughts to Consider
Position:
Unfortunately, research regarding biomechanics is relatively sparse in the sport of ice hockey. Furthermore, most of the research involves acceleration and skating in a straight line. However, the game of hockey involves not just acceleration, but deceleration, stopping, starting, and changing directions. In order to accomplish this, a level of stiffness must exist to support the ankle, specifically for backwards skating. In a journal article from Wu et al. the mechanical differences from forwards to backwards skating were outlined: (Wu, Pearsall, Russell, & Imanaka, 2016)
The skill of backward skating consists of continuous double leg contact
Backward skating (C-Cut) does not have a swing phase only a stance
Forward skating showed greater hip and knee angular velocities compared to backward skating
Trunk segmental angle (relative to the horizontal axis) in forward skating is significantly less than backward skating which indicates that players lean their bodies significantly forward during forward skating
At instant of propulsion skaters showed greater hip, knee and ankle extension movements in forward skating than backwards
Backward skating does not encompass a swing phase, coupled with a larger trunk segmental angle and less joint amplitude with propulsion. In situations like this, stiffness is paramount. I found it interesting that in the Athletic article outlining the fastest skaters in the NHL, of the 12 athletes mentioned, only two were defensemen. Extra mobility, comes at the expense of stability. Something to consider prior to making your eyelet decision.
Body Type:
Muscles:
In terms of muscles and human movement, two of the most important architectural parameters are physiological cross-sectional area (PCSA) and fiber length. Muscle force is proportional to PCSA and muscle velocity is proportional to fiber length. Longer muscles generate higher velocity movement which may be critical for speed on the ice regardless of eyelet preference.
Levers:
Work is equal to force applied over a distance. Thus, if we increase the length of a lever, we can reduce the force needed to get work done. This is the equivalent of riding a bike with a WD40 chain as opposed to riding a bike with a rusty bike chain. Efficiency is maximized with the former, rather than the latter. Another interesting quote from the article came from Dr. Lockwood: “I want to make sure there’s no confusion,” Dr. Lockwood said emphatically in regards to McDavid, who she’s worked with in the past. Conner’s skating is mechanically untouchable. He’s extremely well balanced and a long-levered kid who, based on his mechanics, would be a great skater in skates with no laces.” Eight of the twelve skaters mentioned in the article were over 6’ tall with potentially longer lever systems allowing for increased efficiency on the ice.
McDavid: 6’1” Hall: 6’ 1”
Eichel: 6’2” Zucker: 5’ 11”
Gaudreau: 5’9” Scheifele: 6’ 2”
Mathews: 6’3” Ducliar: 5’ 11”
Tkachuk: 6’3” Reilly: 6’ 1”
Gostisbehre: 5’11” Barzal: 6’
Skating is a skill. As is the case with most questions these days, the answer to skate set up does not reside in a black or white response. My answer as to whether one should skate with their first eyelet untied is….it depends. As a former player myself, I would be less inclined to make this suggestion to a veteran as his nervous system has accommodated and acquired a unique stride signature throughout years of competitive play. In addition, one may argue that the skate is the most important tool for the player to have autonomy over. My response may change when dealing with an intermediate player possessing poor biomechanical traits such as railroading, shallow knee bend and incomplete recovery, or a player who is coming back from injury and has lost a first step. Of course, yet another consideration is position on the ice as both defensemen and goaltenders may need the extra support in the form of stiffness.
Biomechanics plays an important role in each and every sport. It’s important we as performance coaches look at the relevant literature coupled with our own unique pragmatic experience prior to making a rationale decision regarding how to best apply the tools of the trade.
References:
1. Lafontaine, D. (2007). Three-dimensional kinematics of the knee and ankle joints for three consecutive push-offs during ice hockey skating starts. Sports Biomechanics, 6(3), 391-406.
2. McPherson, M. N., Wrigley, A., & Montelpare, W. J. (2004). The biomechanical characteristics of development-age hockey players: Determining the effects of body size on the assessment of skating technique. In Safety in ice hockey: fourth volume: ASTM International.
3. Wu, T., Pearsall, D. J., Russell, P. J., & Imanaka, Y. (2016). Kinematic comparisons between forward and backward skating in ice hockey. Paper presented at the ISBS-Conference Proceedings Archive.
Identifying Shape: Run to Glide in Ice Hockey
I had the opportunity to speak several weeks ago at the SCAPH conference in Phoenix, Arizona. My chat was titled: “From Theory to Ice: Creating a Biomechanical Model for Ice Hockey.” The challenge for me was taking bits of information (much of which has been published in academia) and mixing it with my practical experience and iPhone App (I currently use Dartfish Express but am in the process of trialing a new ai technology) to create a working model. I do believe that kinematic analysis on the ice is critical for return to play. Think about this for a second! Collecting baseline information re: shape and efficiency on the ice may be the most valid measure for performance. Validity is a broad concept but narrowly applied. Validity is context dependent. Context matters. Environment matters. Skill matters.
In creating this working model, I relied on simple heuristics involving shape and efficiency (outlined in blue). Other variables such as limb-length and muscle architecture are important but more difficult to “change” for us as coaches.
Shape = Input, Efficiency = Output
Input + Output = Outcome
Shape
Here is what I look for (much thanks to Stu McMillan):
Pelvis (neutral)
+ Shin angle
Ankles stiff
Acute trunk segmental angle
Split of thighs
How should it look (much thanks to Derek Hansen)
Line 1: Long leaning axis: Enables coaches to view trunk segmental angle, pelvis, and ankles
Line 2: Shank: Enables coaches to view + shin angle and foot strike
Line 3: Hips: Enables coaches to view split of thighs
Line 4: Shoulder: enables coaches to view obliquity between the hips and shoulders
We video, measure and catalogue these shapes. Certainly, no shape is the same, but grossly deviating away from them in the face of injury (after teasing out capacity vs technique issues) may be the future of iterating return to play decisions and improving process outcomes.
Targeted Intervention
Programming is never a perfect science. However, we must seek to understand the basics of scientific experimentation to better serve our athletes and improve our programming skills. Here are the variables of a basic scientific experiment. Think of an experiment as a cause-and-effect proposition.
1) Dependent Variable: The effect. What we are attempting to measure? In the case of programming, the adaptation we are seeking.
2) Independent Variable: The cause. The independent variable is what we are attempting to manipulate to test the effect of the dependent variable. In programming these are what we call the acute programming variables (APV’s): exercise, exercise order, number of sets, intensity, rest, and tempo.
Targeted Intervention
Great scientists/programmers attempt to manipulate as few independent variables as possible. Why may you ask? If we change too many at once, it’s too difficult to decipher what worked and what didn’t. What was the cause? Here is a real-world example. Athlete #1 and Athlete #2 play for the same hockey club. How would you alter their programming? Here are the questions I ask:
How can I maintain the same program within the group while individualizing needs?
How can I manipulate the fewest APV’s to seek the desired adaptation I’m looking for?
Ideas for Action:
Athlete #1 Targeted Intervention: Add two additional working sets to each foundational lift. Measure performance after micro-cycle.
Athlete #2 Targeted Intervention: Eliminate high impulse jumps. Add stretch-shortening plyometrics. Add two additional reps of 10 meter sprints. Measure performance after micro-cycle.
What if the intervention doesn’t work? That’s ok. Many times, it may not. However, the underlying reasoning doesn’t change. Seek to alter minimally, measure, then manage.
Here is a video explaining the science of program design called: A Scientists Guide to Program Design. Targeted intervention is not synonymous with a programming face lift. To the contrary a minimalist approach is necessary.
The tyranny of metrics
I’m passionate about sport. I love the game of ice hockey. I love sports science. Although I operate a private facility, we use numbers daily. We use graphs and displays daily. I’m not against being objective. Our profession is currently operating in an interesting time. The technology/metric craze is upon us as well as the need for coaches to believe they must possess high level statistical/graphical skill sets. Perhaps this is the new norm? Perhaps the speed at which the trend continues will only pick up pace? I’m not against it, selfishly I love to study statistics and learn relevant technologies. However, I believe we need to recognize some underlying assumptions being made and the potential unintended consequences of how our profession may be viewed by the end user, the athlete, in the future
The lecture titled Metric Fixation is part of an 8-series product called The High-Performance Hockey Masterclass. I decided to release this lecture as a value-add for those that have supported my work throughout the years. It’s an ocean out there. One with plenty of noise that continuously seems do deepen the waters. How can we search for signal? What questions should we ask? What matters? How should we choose what matters? How much should we measure? My ideas are outlined in this presentation. Every situation is different, but the need to critically appraise and ask the right questions is paramount. I hope you find this talk helpful.
Ceteris Paribus
How many times as coaches have we heard the following statements?
“In a perfect world”
“All things being equal”
“Apples to apples”
“All things considered”
This is what we have been trained to think like in the academic setting (nothing wrong with this). In essence, this is how statistical tools such as multiple regression work. The goal is to tease out how one independent variable may affect the dependent variable while holding all other independent variables constant. For example, a phycologist may predict college achievement from high school GPA while holding parents’ salary, type of high school (public vs private), extra circular achievement, and access to tutor’s constant. These variables may confound results and muddy the waters between cause and effect. The aforementioned variables all may influence GPA and future collegiate success. However, this enables an apples-to-apples comparison.
Apples and Oranges
Unfortunately, the world is a complex place. Apples to apples comparisons (unless n=1) are next to impossible. More often than not we make apples to oranges comparisons.
Theory and practice are not one the same. Take for example periodization and speed training, both hot topics in today’s strength and conditioning culture. How should we one approach these constructs? Which model? Speed train, or not? In attempting come to a logical conclusion, I choose to to tangle signal from noise using a critical rationalist approach.
Critical rationality is the belief that all answers have to be examined with an eye toward their failure.
Here are a few questions I ask myself via an Onion diagram:
Peel back each layer of the onion in your question asking process.
Teasing out signal from noise is not easy in complex systems. Sure, the textbook is a great place to start, but there is noise all around us. So, which periodization plan should you use? Should you train speed, or not? I don’t have the answer for you, or your athletes, but answering the questions above based on your unique environment is a great place to start.
The NHL Combine: A Quick look at the Research
(This article was originally written in 2019)
It’s that time of year once again. A time when 100+ of the world’s best young hockey players come together to take place in the NHL combine. Testing, interviews, meetings and assessments all strategically designed in order to further streamline managements draft day decision making. The tricky part (aside from evaluating on ice skill and character) is deducing which off-ice tests best transfer into on ice performance.
The purpose of this brief article was to explore the research to see if there is an off-ice test(s) that best transfers into on ice performance. This review explored 5 research articles.
Off -Ice Performance and Draft Status of Elite Ice Hockey Players [1]
Question: Could tests performed at the combine distinguish draft status? This was a retrospective cross-sectional study design with performance and draft order from 2001-2003.
Findings: No significant effect of off-ice performance on draft selection. No single test could distinguish between draft rounds in the 3 years sampled. “It appears off ice tests cannot accurately predict ice hockey playing ability in an elite group of athletes.” Perhaps other variables such as on-ice skating ability and hockey sense weigh more heavily in the decision-making process for NHL GM’s and coaches.
Positional Performance Profiling of Elite Ice Hockey Players [2]
Question: Is positional profiling for Elite hockey players possible by examining anthropometric and physiological performance? This was a retrospective cross-sectional study design using the same pool of data mentioned above.
Findings: 1) Defenseman were heavier and taller than both forwards and goaltenders. Defensemen performed more reps on the bench press than both forward and goaltenders. 2) Goaltenders showed greater body-fat percentage compared with forwards. Goaltenders had greater flexibility, but lower upper body strength measures (bench press, push-ups) and aerobic capacity compared with at least one other position. No positional differences were discerned from broad jump, vertical jump, aerobic power and curl-ups. The research supports the use of anthropometric measurements, upper body strength, and anaerobic capacity to distinguish among positions in elite level ice hockey.
Relationship of Physical Fitness Test Results and Hockey Playing Potential in Elite-Level Ice Hockey Players [3]
Question: What fitness capacities predict hockey playing potential at the elite level (determined by entry draft order)? Combine results were compared to draft selection order on a total of 853 players.
Findings: Regression models revealed peak anaerobic output to be important for higher draft selection in all positions. Removal of goaltenders from the revealed standing long jump a significant predictor variable for both forwards and defensemen.
The Usefulness and Reliability of Fitness Testing Protocols For Ice Hockey Players: A Literature Review [4]
Findings: “The majority of studies (15) in this review suggest that off-ice testing has limited use when testing ice hockey players.” It is proposed that ice hockey adhere to more specific testing measures both on, and off the ice.
Importance of Body Composition in the National Hockey League Combine Physiological Assessments [5]
Question: To investigate the importance of body composition in the battery of combine physical tests
Findings: All participants underwent combine testing as well as a dual energy x-ray absorptiometry scan to measure boy comp. Partial correlations were used to explore the relationship among body comp and combine testing. In 4 of the 6 strength/power tests (Wingate, long jump, bench press, and both grip strengths), lower and upper lean tissue explained significant amounts of variance.
Key Takeaways:
Most off-ice tests show weak correlation to on-ice ability.
My thoughts: Hockey is played on the ice in a near frictionless environment. This environment affects the neuromuscular system and the requisite muscles needed for movement. Running and skating are very different bio-motor abilities. There’s a reason Lance Armstrong finished 868th at the 2006 New York City Marathon. His neuromuscular system adapted to the unique demands of cycling, not running, regardless of his VO2max.
Draft skill and character, off-ice testing comes second
My thoughts. There doesn’t appear to be near enough evidence to correlate any unique test to on-ice ability as a hockey player. Hockey is a unique game of read and react, anticipation, acceleration, change of direction and resiliency. The best test is the game itself. Scouting plays a critical role in this process.
Do combine numbers matter?
My thoughts. I sit on both sides of the fence. On one hand, great numbers may indicate dedication, commitment and work ethic. On the other it may just mean some young men have matured faster than others. Finally, many of these young athletes have zero to minimal training ages in the gym. In this case poor numbers mean nothing more than a higher ceiling in the future.
1. Vescovi, J.D., et al., Off-ice performance and draft status of elite ice hockey players. International journal of sports physiology and performance, 2006. 1(3): p. 207-221.
2. Vescovi, J.D., T.M. Murray, and J.L. VanHeest, Positional performance profiling of elite ice hockey players. International journal of sports physiology and performance, 2006. 1(2): p. 84-94.
3. Burr, J.F., et al., Relationship of physical fitness test results and hockey playing potential in elite-level ice hockey players. The Journal of Strength & Conditioning Research, 2008. 22(5): p. 1535-1543.
4. Nightingale, S.C., S. Miller, and A. Turner, The usefulness and reliability of fitness testing protocols for ice hockey players: A literature review. The Journal of Strength & Conditioning Research, 2013. 27(6): p. 1742-1748.
5. Chiarlitti, N.A., et al., Importance of Body Composition in the National Hockey League Combine Physiological Assessments. The Journal of Strength & Conditioning Research, 2018. 32(11): p. 3135-3142.
The Jaggedness Principle: Game Speed
I read an interesting book recently entitled “The End of Average” by Todd Rose. The book outlines issues pertaining to using the average measure as a benchmark for performance, fit and evaluation in complex systems. One of the guiding principles that he outlined in the book was called the jaggedness principle. Jaggedness is the idea that "we cannot apply one-dimensional thinking to understand something that is complex and jagged." In this situation, he defines "jagged" as the characteristic of having many weakly-related dimensions.
In this context, what immediately came to mind was the concept of game speed in team sports such as ice hockey. What is it? How is it defined? What do we measure? What can’t we measure? I had a fantastic conversation recently with my friend Stu McMillian on the HPH Podcast about the many layers of speed (capacity, ability, and skill). This conversation brought to my mind many more questions than answers. Having read Todd’s book, I attempted to untangle a few of these thoughts.
Game speed is a latent concept so I attempted to provide a definition that I could use as a reference: The ability to process, anticipate, and respond to ever-changing conditions on the ice as quickly and efficiently as possible.
Output (Efferent) vs Input (Afferent)
A question I often ponder is at what point is strong enough? At what point is fast enough? You could say that as performance professionals we build efferent beasts (output), but some of the best hockey players aren’t the strongest, or the fastest, although they do take their training seriously. Perhaps the “best of the best” are afferent (input) beasts who use their strength and speed wisely, position themselves appropriately and are much more efficient in choosing the appropriate motor task to accommodate the changing environmental conditions. Strength and speed training is paramount in sharpening the sword and should be part and parcel of the process; however, the coordination and response to the environment to use this strength and speed may separate the average from the elite.
Jaggedness
Game speed is not one dimensional. There are many variables at play (see image below). As coaches, we are great at measuring outputs (linear speed, power, strength), but it’s the inputs and outcomes that are most difficult and important to consider/contextualize in team sport. Which player would you choose? Player #1 has high hockey IQ, processing speed and tactical awareness, but below average strength and top speed. Player #2 has fantastic top end speed, and acceleration, but poor processing speed and hockey sense. Who’s faster? Their hypothetical weighted scores are identical. Who would you want on your team? Who has better game speed? For me, I’d take player #1 any day. As Anatoly Tarasov once said "speed of hand, speed of foot, speed of mind. The most important of these is speed of mind.” It doesn’t matter how fast you get to the wrong place.
To fix or Not to fix
All things being equal (which rarely are), the solution to fixing these issues may be more easily attained in Player #1. The questions posed by the coach may be:
To Fix
Which of the ‘below average’ attributes have the greatest POTENTIAL to be developed? (In some players certain attributes have greater potential than others)
Current injury restricts that may restrict these abilities
Anthropometrics
Playing position
Age/level: Is the juice worth the squeeze?
Not to Fix
My friend Fergus Connolly states: “Just because something is below average does NOT mean it should be automatically developed or improved because they all operate in concert with one another as a complex dynamic system. One input changes everything .” Elite players are master compensators and have spent years building motor efficiency at their craft.
“Complex problems cannot be solved because any attempt to create a solution changes the nature of the problem.”
Getting to top speed on the ice is much more than just sprinting in a linear fashion. It’s about the tangible intangibles of hockey sense, processing speed, tactical awareness and compete level. Speed in this sense is slowing the game down, not speeding it up. These attributes are best harnessed on the ice, or in the video room. The sport car analogy paints a beautiful picture. The car (efferent/output) and driver (afferent/input) are two pieces of the game speed puzzle. Both are important, but we mustn’t confuse the Ferrari engine with the amateur driver. Train speed, train strength, train power, but respect the jaggedness of the concept and the drivers experience.
Low Hanging Fruit
I spent the first decade of my coaching career believing that strength was the ultimate cure for just about everything and everyone in team sport. Want to be a better player? Strength train! Want to get faster? Strength train! Want to be more resilient? Strength train! Sooner or later, I realized if the answer to every problem was increasing squat, bench, and Olympic lift numbers, we’d have a world full of professional athletes. Well, that certainly isn’t the case.
As I’ve aged my opinion has changed. KPI anchors are very different among the developmental spectrum in team sport. The fruit tree is a beautiful illustration of the importance from beginner to intermediate to master of sport regarding “what matters” and “what counts”.
Don’t Confuse Low Hanging Fruit with a Bountiful Harvest
For the beginner everything works. Young Tommy who has never strength trained before has the adaptation reserve of an SUV on empty. Proper movement efficiency coupled with strength training is a miracle drug. Ground fruit is the food of choice. This isn’t the case for a team sport athlete with a large training age making a living in sport. The question we must ask is where to prioritize our time with the end goal of improving performance. The fruit that bears the best return is the middle, and difficult to reach fruit. This comes at a physiological cost and affects programming. In fact, I wrote an entire book on it called The Gain, Go, Grow Manual: Programming for High Performance Hockey Players. This fruit is also the hardest to measure. A feeling that brings great discomfort in the coaching profession. As my friend Fergus Connolly says: “start with the scoreboard and work backwards.”
Skating, like playing the guitar is a skill. None would say “take the entire summer off, strength train your fingers, and be prepared to play in front of a sold-out concert come August.” Dexterity, rhythm, pace, progression, synchronization and chord mastery are all skills. Perhaps the best place to practice these skills is also the most difficult to reach for? As a young coach, I made the mistake of confusing low hanging fruit with a bountiful harvest.
Not everything that can be counted, counts, and not everything that counts can be counted. One of the many lessons I’ve learned in my coaching journey. Choose your fruit wisely.
Show me the Evidence
Last week’s blog inspired a long discussion based on a quote from a friend re: using advanced training means on developmental athletes early in the off-season. The quote states: “It cheats the athlete twice. Firstly, using advanced means when a basic mean can create similar adaptation, and second, when it’s time to use the advanced mean, it’s no longer a new stimulus to the athlete.” A long Twitter debate ensued on the subject matter of using advanced training means for developmental athletes and and potential compromised adaptation in the future. Inevitably someone essentially said, “show me the evidence.” This got me thinking. Where would we find such evidence? Who would run a study like this? What kind of evidence do we need as practitioners? Does it need to be published to be “proven?” At its essence, what is science?
From my experience in both in the pragmatic setting, and at the graduate level in academia, “evidence” is not the same. I’ve written more about this in a previous blog post. As coaches we perform observational research. Very similar to Texas Sharpshooters. We look for clues (bullet holes), observe trends, look for patterns, and hypothesize (circle the target). We also perform N=1 research on individuals within the group/team. The most important data we can pull from is longitudinal data from our unique sample. Sampling bias is a major issue in all forms of academic research, so for the performance coach the gold standard is the team, our athletes, our individuals, and our data. This is what drives decision making.
Academic research in sports science, in my opinion, is not the gold standard for evidence for most practitioners but it is important. The goal is deductive in nature (hypothesize – collect data - infer). The internal and external validity of performing work in a lab while attempting to control for confounders is extremely different than the weight room, the pitch, or on the ice. The two worlds are not the same, and most certainly not the athletes you’re working with. This is not ceteris paribus, it’s more like apples to oranges. Finally, just because something is published, doesn’t mean it’s “proven.” There is a replication crisis happening today in scientific fields such as molecular biology and traditional epidemiology with much more scrutiny than sports science. John Ionnidis’s article titled “Why Most Published Research Findings are False” is an interesting read. He states: “for many current scientific fields, claimed research findings may simply be accurate measures of the prevailing bias.” This may be due to small sample size, poor power, and smaller effect size. All issues plaguing sports science.
Science is not about truth, it’s about conjecture and refutation. It’s about getting closer to the approximation of “truth” through a temporary theory/hypothesis. Temporary theories are superior in two ways: 1) they provide superior explanation, 2) they are tested more frequently. So, what is the evidence? Is it a p of <.05? Is it a large effect size? Is it a great study design? Is it because the results were published in a reputable journal? I don’t claim to know the answers to these questions, but the first place I’d start is with the athletes I train, in the environment in which they play.
Optimal vs Capable
The off-season is officially here (minus those still deep in playoff runs). Many players engage in active recovery prior to starting their first training block at our facility. From my experience, this is a sensitive time frame within the overall process. The first block of training is a time where many young coaches make mistakes (I know I sure did). Optimal vs Capable is an idea that I stole from renowned coach and author Ian King (highly recommend his books). He states:
“Don’t focus on how hard you can train, rather focus on how hard you should train.”
I’ll never forget reading this as a young coach and the impact that it made on me since. Well, how hard should you train? That’s the logical question. To answer it we need some context and a basic understanding of physiology.
Context: Most players take at least 2-3 weeks off prior to starting their programs, coupled with a long grinding schedule during the regular season which makes it difficult to consistently strength train.
Physiology: Delayed onset muscle soreness (DOMS) typically occurs for one of two reasons: 1) extended time away from training, 2) being too aggressive, too soon. Yes, DOMS occurs during the off-season, but minimizing it at the onset enables density and frequency within the program. It creates consistency. This is critical.
“For every substance a small dose stimulates, a moderate dose inhibits, and a large dose kills.”
Dosing stress is no different. Below are several programming heuristics we’ve developed over the years to address the early off-season. Simplicity is the ultimate sophistication.
Unwind
No Silver Bullets
The Big 2: Acute Programming Variables (APV’s)
Play the Guitar
Unwind
The first several weeks of training are used to counter the chronic posture of the sport. Hockey is played in a heavily anterior chain position. How can we unwind this posture? We focus on short to long isometrics. We work antagonists such as the hamstring and posterior shoulder girdle in a shortened position, while lengthening the anterior chain using isometrics. Our tissue remodeling block is described in greater detail in by book, the Gain, Go, Grow Manual. For a detailed understanding of tendons and training means I would strongly recommend this listen from Dr. Keith Barr.
No Silver Bullets
Bands, chains, drop jumps, oscillatory lifts, and specialty bars (unless for orthoepic need) are silver bullets. As coaches we only have a few silver bullets in our coaching holster. Why would we want to use them up so soon? As my friend Keir Wenham Flatt says, “It cheats the athlete twice.” Firstly, using advanced means when a basic mean can create similar adaptation, and second, when it’s time to use the advanced mean, it’s no longer a new stimulus to the athlete. Simple heuristic, don’t use silver bullets during this phase.
The Big 2: APV’s
As coaches, we dose stress. Unlike the chaos of team sport, the weight room is a relatively stable environment. We as coaches have a programming toolbox. Our tools are called acute programming variables (APV’s). Here they are:
1.) Exercise
2.) Exercise Placement
3.) Intensity
4.) Number of Sets
5.) Rest
6.) Tempo
The big two we focus on early and often are exercise selection and tempo. Nothing sexy regarding selection, but basic. For upper body lifts we use dumbbells as hand grip controls humeral head orientation and provides a more orthopedic friendly experience. For lower body, goblet holds for lifts such as split squat reflexively engage the rectus abdominus (see unwinding from above).
Tempo is another variable we focus on for tendon tolerance. The goal with isometric work is to attain a CREEP effect of the tissue (the muscle engages while the tendon lengthens). We typically program 3-4 sets of :30 second holds. Controlled tempo is a big focus during this block.
Play the Guitar
The best guitar players master basic chords and focus on skill acquisition. Strength is a skill, so is playing the guitar. No one says, “great guitar lesson, I threw up and I can’t feel my fingers.” Great lessons enhance coordination, dexterity, strength, and endurance.
The first training block of the off-season should be relatively simple and progressive in nature. It’s a slow cook process, not the microwave! It’s not a time to show off advanced training means and hammer players with large doses of stress. The goal is to be progressive in nature while creating sustainability in programming.
Worm’s Eye View vs. The Bird’s Eye View
Perhaps the evolution of critical thinking skills naturally starts with being passionately curious in a self-chosen domain. Often young coaches are eager to learn from what one would call the worms eye view.
Worms Eye View: In this world, the young coach has a granular view looking up from down below.
From my personal experience, this starts the evolution of what I call first principles knowledge. Specific understanding of fundamental principles such as biomechanics, adaptation, and programming. All critical elements for the practitioner to procure. However, at some point in the quest for developing critical faculties, boundaries need to be pushed beyond first principles. As coaches progress their content knowledge, new views emerge.
The Birds Eye View: In this world, the coach has an aerial view looking down from high above.
The questions now become: how can we remove the silos of knowledge we have accumulated? What other domains can we pull/learn from? Philosophy, statistics, economics, psychology? How do we become Swiss army knives of knowledge, serial specialists that view the performance landscape as vast and rich?
The reality is that both views are needed at different stages of the coaching process. As a young coach, I was eager to specialize. I viewed the performance landscape with horse blinders. I dug many deep holes, and at times lost site of the interwoven connections. Most, if not all, of my education was content specific. Fast forward to the present and my view has changed. Perhaps it was reading the likes of Karl Popper, Gerd Gigerenzer, or listening to the likes of Coach Dan Pfaff or Russ Roberts. Most of my continuing education these days has nothing, but everything to do with performance. I would like to think I view the performance landscape today with dragonfly vision.
Full Circle
I recently went back to school (from a 17-year hiatus) as a 40-year-old PhD student. My view changed once again. The worm had reemerged. Targeted readings, specialization, and study. It was a phenomenal experience as it taught me so many valuable insights and evolved my skeptical lens. Perhaps the best summation came from a joke I heard regarding the PhD process: “You learn more and more, about less and less, until you know absolutely everything, about nothing.” A joke that you could only get with birds’ eye vision.
Both views are important. The worms eye view provides the “brick” while the birds eye view provides the “mortar.” This is the structure and foundation of critical thinking.
The Fast and Frugal Tree: Making Decisions in the world of Uncertainty
The fast and frugal tree (FFT) was an idea that I was first introduced to from Gerd Gigerenzer in his fabulous book Gut Feelings. The book outlines key concepts in bettering our decision making in times of uncertainty. Physiology, scoreboard success in team sport and phycology live in this unique world. So too, the world of injury and return from injury. Gerd states: “In an uncertain world, a complex strategy can fail exactly because it explains too much in hindsight.” Heuristics, coupled with deep contextual understanding, and objective information steer the ship.
Below is a presentation that I gave as part of the Altis Mentorship Phase III Mastery with Coach Dan Pfaff (highly recommend) outlining the potential use of Frugal trees in steering return to play decisions. More information can be found in the High Performance Hockey Masterclass resource.
Ad Hoc, post hoc, coaching and sports science
Critical thinking is the art and science of rationalism. It’s the foundational belief that science starts and ends with problems. The goal of the pursuit is iterative in approach with each failure leading closer and closer to a better explanation. The result is a temporary theory or conjecture. Supreme theories offer superior explanations and are tested/criticized more often. The “game” of science is a game with no end.
Critical thinking is context specific in the world of coaching and sports science. The major reason for this is the absence, or presence of time in making decisions. According to Meyer [1]:
“Sport scientists try to reduce the number of variables influencing an outcome so that causation can be more easily disentangled. Scientists look for answers that are right or wrong, and time is not of the essence.”
This form of logic is called deductive reasoning. The scientist starts with a premise (hypothesis, data, information etc.), and concludes by making an inference (a result). The key with deductive logic is that the scientist does not move beyond the premise and make broad based inference. (Hypothesis - Data Collection - Data Analyzation - Conclusion)
On the other hand, sport coaches do not get the luxury of time. Many times, decisions need to be made well before all the facts are in. Coaches’ are more concerned about solving immediate performance problems, rather than determining causation [2]. Coaches are influenced by tradition advice from mentors, and imitation from other successful colleagues [3,4].
This form of logic is called inductive reasoning (reasoning by repetition). The coach starts with a premise (immediate information, video, game flow etc.), and reaches a conclusion. In the case of the sport coach, he/she moves beyond the premise and creates “heuristics” (simple rules of thumb). Heuristics are mental short cuts that minimize time-based decision making. Heuristics are based on years of practical experience, and detailed content knowledge.
Coaches and scientists may not see the performance world through the same lens. This has to do with previous experience and bias (we all have it!). Each environment has its own inherent constraints. In the private sector, our goal is to invest in technology that best measures the unique demands of sport (hard to do with hockey), is reliable, and easy to interpret and analyze (Body Fat testing, Laser Speed Testing, Force Plate testing, Bar Speed testing, TRIMP). Our goal is to communicate results to our pro athletes within twenty-four hours and our team sport coaches within 48-72 hours. We use simple charts and graphs to communicate. We are also use analogies when communicating data. Here’s an example:
Example 1:
Sport Scientist/Coach: “We spoke last session re: TRIMP. Remember that analogy that we used? If you drive from Columbus to Cincinnati, TRIMP is your total gas mileage. Looks like you’re getting more efficient as the same workout has used less gas this trip.
Athlete: Thanks Coach.
Example 2:
Sport Scientist: We have all the baseline asymmetry data collected for our hockey club. We flagged any concerns in red as these numbers are greater than what current research states may be acceptable? Take a look at the asymmetry graph.
Coach: What do you mean acceptable? Why is this important? How will it drive scoreboard success?
Sports Scientist: Great question coach! It’s important because the best ability is availability. We want healthy players.Current research suggests that a > 15% difference between force production in both left and right legs may predispose an athlete to an increase chance of injury and/or a decrease in performance. Picture eating a meal on a 4-legged dinner table, if I remove one leg, the table becomes compromised and unbalanced.
We can get an awful lot of information out of this “test” without physical wear and tear on the athlete. We can also use these numbers in the case of injury to reference previous numbers.
Coach: Ok. Great!
At the end of the day, the goal is to allow open communication, while considering the unique background, bias, and previous experience of key decision makers. Every environment is different. However, a working understanding of each member allows for the integration and cooperation among team members. This is how we operate in the private sector.
References:
Meyer, C. Science, Medicine and the US Common Law Courts. In Expert Witnessing, Routledge: 2020; pp. 1-29.
Sands, W. Communicating with coaches: envisioning data. Applied Proceedings: Acrobatics 2005, 11-20.
Sands, B. Coaching women's gymnastics; Human Kinetics Publishers: 1984.
Sands, W. The role of difficulty in the development of the young gymnast. Technique 1994, 14, 12-14.
Resisted Sled Sprint Training: A Practical Approach in the Private Sector
One of the biggest programming changes we’ve made at DSC over the last year is the use of resisted sled sprint training. We have long understood the value of sprint training for our hockey population as off-ice speed is a moderate/strong correlate to on-ice speed, but we never truly appreciated the value of resisted sled sprint training. Essentially resisted sled sprint training encompasses towing or dragging an external resistance with the intent of moving at top speed. For a beautiful article explaining the science and practice of resisted sled sprint training, I would strongly recommend reading Resisted Sled Sprint Training – A Practical Guide to Improve Short Sprint Performance written by my friend Lassi Laakso. Here are a few thoughts on why, where, and how we incorporate resisted sled sprint training in the private sector.
Why?
From a kinematic standpoint, ice hockey is a unique game. The goal is to minimize air friction by staying low during stance. In addition, players wear hockey skates to compete, which places varying demands on muscle contraction (isometric/concentric emphasis), and ground contact times (> impulse) experienced on the ice. The table below provided by Henk Krajjenhof is a fantastic visual of the ground contact times, muscle contraction type and bio-motor ability needed during training to compete in a 100-meter sprint. It’s important to note the take off and acceleration columns, and the difference in ground contact times and muscle contraction type when compared to max speed efforts. Foot contact time decreases as speed increases. This is the opposite of what happens on the ice.
From a kinematic standpoint resisted sled sprint training closely mimics on-ice skating efforts.
Relevant Research (1)
Aim: to compare common off-ice fitness tests and off-ice resisted sprints for predicting 15-m on-ice skate time.
Sample: 52 Canadian Varsity Hockey Players (male and female)
Findings: off-ice sprint times against resistive loads of 15 and 30 kg present a strong relationship with on-ice skate time (r. = > 0.7). (Note: resisted on and off-ice sprints were measured with the 1080 Sprint device).
Where?
A more detailed description of our gain, grow, go programming can be found here. Below is an example of how we dose our off-season jump and speed training for hockey players.
Day 1: Gain: Max Effort Day: This is complimented with high impulse jump training and resisted sled sprint training. Impulse = high force/moderate time
Day 2: Go: Dynamic Effort Day complimented with true plyometrics and unloaded speed work. Impulse = high force/low time
Day 3: Grow: Repeat Effort Day complimented with frontal plane jump training. Impulse = high force/high time
The > the time constraint, the more important RFD becomes
The < the time constraint, the more important force output becomes
Our training means are complimentary with the goal of pairing foot contact profiles (sprinting and jumping) to weight room focus. Therefore, resisted sled sprints fit the bill for Gain Day which starts the week. In terms of intra-session placement, resisted sled sprint training is programmed as a first priority, followed by jump training and weights.
How?
One of the principles that dictate our program design at DSC, is the principle of overload. Resisted sled sprint training is no different, especially for the developmental athlete. Here is how we program resisted sled sprints using velocity decrement as a loading parameter. For more information, I strongly suggest listening to Ken Clark’s episode of the Pacey Performance Podcast.
1) Time the athlete without any external resistance. For example, the athlete runs a 1.34 10-yard sprint
2) Start training with an empty sled. Time accordingly
3) Add 5% Vdec each week. Based on the athlete above, a 5% Vec would be (1.34+.067 = 1.407)
4) Add 5% Vdec each week progressively
5) Stop/hit sweet spot at 50% Vdec
** For our pro group of 5-8 athletes, all loads are individualized
** Recommendation for large groups: loads based on group average for time efficiency
Resisted sled sprint training is a fantastic tool in the hockey players training arsenal. The keys are where to place it, and how to load it. As with any exercise the proper application is crucial to long term success and sustainability.
References:
1. Thompson KM, Safadie A, Ford J, and Burr JF. Off-ice resisted sprints best predict all-out skating performance in varsity hockey players. J Strength Cond Res, 2020.
“Hockey Specific”
“Train the athlete, not the sport!” “We are general physical preparation practitioners. Specific physical preparation takes place on the ice, not the weight room.” While I generally agree with both statements, I do feel that a deep understanding of sport at a first principles level aids in developing much more specific programs. What do I mean first principles? Biomechanics, adaptation, sport acumen and program design are first order principles. They provide deep contextual understanding of the pieces at play in program design. They are the mortar to each brick we place as coaches in the hopes constructing a program. So, what is sports-specific? The goal is to conceptualize a working definition. According to Dictionary.com both “sport” and “specific” can be defined as:
Sport: an athletic activity requiring skill or physical prowess and often of a competitive nature
Specific: having a special application, bearing, or reference; specifying, explicit, or definite
Sport + Specific = an athletic activity requiring skill or physical prowess (with a training program designed) having a special application, bearing, or reference, specifying, explicit, or definite
Now, let me be very clear, nothing will ever replace the ice. The forces, the speed, the playing environment are all unique. Hockey specific is the game itself. There is no substitute. I have written about this in great detail in my most recent book, The Gain, Go, Grow Manual.
Applied forces on the ice will never be re-created in the weight room.
However, I do believe we can build programs for hockey players that better serve their sport and playing position. It all comes down to first principles.
Biomechanics:
When it comes to programming, we must ask ourselves:
How does one move on the ice?
Is there differences between locomotion on the ice vs land? What are the gas pedals? What are the brakes?
What are the injury drivers?
Can we affect programming?
Ice vs Land: Biomechanical Breakdown.
Video courtesy of: Apex Skating.
Coaches Eye: Environment dictates locomotion. The ice has minimal friction. This changes the gas pedals and breaks used in locomotion on the ice. Watch the video in slow motion. Stop the video during propulsion (full extension). Close up on the hip, knees and ankles. The drivers are the powerful glues, and quads. While the breaks are the adductors (longus and magnus). The hips are externally rotated. Now, stop the video during recovery. The drivers and gas pedals are reversed while the hip internally rotates. Skating is a heavily frontal/transervse plane activity.
Below are several articles/blog posts to consider that attempt to answer these questions:
Performance Implications for Ice Hockey
What Makes an Efficient Skater
Injury Rates in the Sport of Ice Hockey
Key Takeaways:
Understand the unique biomechanics of the stride
How can we train the gas pedals in a logical, progressive manner?
How can we train the brakes in a logical, progressive manner?
What are the most common injuries that occur on the ice? Can we take a pre-mortem approach and better our programming efforts in a more specific manner?
Adaptation:
When it comes to programming, we must ask ourselves:
What adaptation(s) are we attempting to solicit?
What methods will we use to target this adaptation?
What buckets can we fill that are NOT being filled on the ice?
Can we affect programming?
If the goal of the performance coach is to fill buckets that are not being filled on the ice, owning the sagittal plane would be a good start. Think about this for a moment, you can’t produce a force parallel to the plane of contact on the ice. This is frontal/transverse plane living! So, how does this effect programming? Nothing drastic, but there are subtle changes. Here are a few:
Beautiful video of of the hip moving into internal and external rotation. Coaches Eye: Which fibers surrounding the hip can we work that may not full buckets on the ice?
1) Corrective Strategies: Sagittal plane facilitation of the glutes. Work them as hip extensors, not abductors or external rotators. The glutes get plenty of work both as abductors and external rotators on the ice. Master the sagittal plane. Closed chain adductor activities facilitating the adductor magnus, 90/90 hamstring work focusing on muscle activation in the sagittal plane. Rectus abdominus work early and often in attempts to balance the pelvic girdle.
2) Weight Focus: Sagittal plane focus for major lifts early in the off-season (bi-lateral lifts), slowly shifting to more frontal/transverse plane (SL lifts) during later portions of the off-season. Sagittal plane med ball power work during the regular season, limit frontal plane loading during the hockey season as these buckets are being filled on the ice.
3) Isometrics: Early summer focus on tissue remodeling in the sagittal plane. Think of this as unwinding from the chronic position of sport. The methods used are long duration yielding isometrics in which a player holds a posture for time (typically 2:00 in length, which may be broken up into 4 x :30, and short/long isometrics in which an agonist is held in lengthened position for time while its functional antagonist is held in a shortened position. For the high-caliber hockey player, functional antagonists such as the hamstrings and the posterior shoulder complex are facilitated and held in a shortened position, while the hip flexors, the anterior shoulder, and ankle dorsiflexors are held in a lengthened position
Yielding elevated split squat. The targeted adaptation is creep of the myo-tendinous junction of the rear hip flexors with co-contraction of the ipsilateral glue max.
4) Mobility: The chronic position of the sport creates length tension issues at the hip joint. The femur orients internally while the pelvis tips in an anterior fashion. Properly mobilizing the joint during the off-season is an important adaptational consideration for the performance professional.
The vectors of the various adductor musculature. The “hockey players tug of war” creates pull on one side (adductors) relative to the other side (rectus abdoninus) of the pelvis. Photo courtesy of the High Performance Hockey Masterclass.
The hockey players “tug of war.” Photo courtesy of the High Performance Hockey Masterclass.
Hip Distraction: the goal is to create a postero-lateral mobilization effectively creating a better “fit” in the acetabulum.
5) Tissue Specifics: Hockey patterns in the late off-season to reinforce the tissues prior to ice touches.
Conversely, understanding the unique intra-muscular adaptation that occurs on the ice is also extremely important. These are peripheral adaptations that are specific in nature. Nothing can replace the ice. Why is this important? First principal knowledge allows us to understand that what’s omitted in the program is just as important. Sport is king for conditioning.
Below is an article/blog post to consider that attempts to describe the unique demands of the ice:
Key Takeaways:
What adaptations are we attempting to accomplish? Are they specific to the sport?
What are the length tension issues? Can we release the “tug of war” using specific means?
The hip is to hockey as the shoulder is to baseball. How can we best approach hip care in a specific manner?
How can we best condition for central adaptation?
Peripheral adaptation is specific! Nothing takes place of the ice.
Sport Acumen:
When it comes to sport acumen, we must ask ourselves:
What are the technical aspects of the game?
What are the positional demands? Goalies’ vs Forwards vs Defensemen?
What are the tools of the trade?
Can we affect programming?
Although I feel being a former player is a favorable pre-requisite, there are some great resources to learn more about the technical and tactical aspects of the game. I strongly recommend Coach Dave King’s new book Loose Pucks and Ice Bags. It provides a simple, yet sophisticated look at both the history and technical elements of the game.
In addition, what are the positional differences? What needs to be added/omitted based on these differences?
The reverse vertical horizontal (RVH) position in goaltending places tremendous stress on the hips. What means need to be added? What means need to be omitted?
Below is an article/blog post to consider that attempts to describe the tools of the trade:
Tools of the Trade: The Hockey Skate
Key Takeaways:
Understanding positional demands is important in creating targeted interventions
What needs to be added? Omitted?
Technical/tactical elements aid in understanding how the player competes in his/her environment. What kind of a player are they? What are their strengths? Weaknesses on the ice?
Program Design:
When it comes to program design, we must ask ourselves:
What are the acute programming variables?
How can we manipulate these to be more specific to sport?
Can we affect programming?
Acute Programming Variables:
1.) Exercise
2.) Exercise Placement
3.) Intensity (Bar speed)
4.) Number of Sets
5.) Rest
6.) Tempo
Variables such as exercise, intensity and rest may vary considerably depending on the adaptation we are chasing, the age of the athlete and the time of year.
Management vs Development. Programming considerations for ice hockey. How does the performance coach adjust APV’s?
Once we have adjusted our acute program variables to reach our desired adaption, the next logical question is, how do we progress them. There are 3 ways we can progress the acute programing variables:
1.) Progressive Overload (lift more weight)
2.) Variety (change the exercise, bar, stance, joint angle, implement, contraction type)
3.) Specificity (focus on patterns or ESD similar to sport)
Programing is both an art and a science, but it doesn’t have to be rocket science. Prior to commencing always ask yourself: “What am I going after? How will I get there? Of the controllable variables that I have at my disposal as a coach, which ones will I use? Why, and how can I communicate this to the athlete, coach or client?”
Below are several articles/blog posts to consider that attempts to describe program design implications:
Programming Considerations for Ice Hockey: Part 1
Dosing Volume Using the High/Low Model in Ice Hockey
Key Takeaways:
What time of year is it?
Can we pull the fire alarm without burning the house down?
How can we manipulate the APV’s based on time of year and training age?
So, is there such thing as a hockey specific program? Based on the definition I provided above; I believe there is! Nothing will ever replace the ice, but I do believe that we as coaches can better prepare our athlete with a sound understanding and application of first principles.
The High Performance Hockey MasterClass is a comprehensive 8-part lecture series exploring the science and practice of high performers in the sport of ice hockey.
Logical Fallacies: The Art of Argument
Logic is the science of valid inference. Inference simply means conclusion. In other words, is the conclusion logical? An argument is composed of both a premise, or premises (assumptions, data, numbers) and a conclusion.
Argument = Premises + Conclusion
In a good argument, the conclusion follows from the premises; in a bad argument, it does not. The premises may be irrelevant to the conclusion, the conclusion may be much stronger than the premises, or the inferences drawn violate basic rules of logic. When arguments go astray, someone has committed a fallacy. There are two types of logical fallacies.
Formal fallacy: an argument is false based on its structure
Informal fallacy: an argument is false based on the content of either the premise or conclusion
Take a look on social media these days and chances are someone has committed an informal fallacy. Here are a few of my favorites that I see re: the strength and conditioning industry.
Hypothetical argument: Squatting heavy increases sprint performance (or sports performance).
Ad hominem fallacy (attack of character): “Heavy squatting increases sprint performance, the only coach who disagrees with me doesn’t hold any weight in the industry because of his/her political views and is performs X training.”
Appeal to Authority (ad verecundium): “Yes, yes, of course heavy squatting decreases sprint times because (Insert Authority figure here) said so.”
Biased Sample: “I’ve seen it time and time again, heavy squatting increases sprint performance.” (The coach did not mention that the sample consisted of youth athletes with minimal training age).
Cherry Picking: “Here is the evidence (article 1, 2, 3 etc.) that heavy squatting increases sprint performance (while failing to mention those who didn’t see results in the study OR other studies with null results).”
Post Hoc Ergo Propter Hoc: “Since sprinting faster followed heavy squats, therefore, heavy squats increase sprint performance. (Since event Y followed event X, event Y must have been caused by event X)
Red Herring: (Derailing the argument by bringing considerations that are irrelevant or out of context). “I’ve seen one legged BOSU ball, balancing acts, but the only exercise that improves sprint performance is heavy squatting”.
Straw Man: (Misrepresenting an opponent’s argument). An opponent states: “There are plenty of alternatives that may increase sprint performance, such as technique, actually sprinting and single leg alternatives.” The individual responds: “So you don’t want to get fast? Isn’t speed paramount in team sport performance? Maybe your athletes don’t want to get fast?”
Sir Karl Popper was found of stating that superior theories (aka inferences/conclusions) are the result of superior explanations (difficult to falsify) and those that are better tested/criticized. Understanding informal logical fallacies enables individuals to better scrutinize explanations. The end goal may not be to engage in debate, but to improve the BS meter in unpacking arguments. The burden of “proof” should lie in those asserting the statement. However, this certainly most always is not the case. Personally, I live by Brandolini’s Law (the time it takes to refute bullshit, far exceeds the time it takes to produce it). However, spending time improving your critical thinking skills is a gift that keeps on giving! Better understanding informal logical fallacies is a great place to start!
Problem, Process, Presentation: Selecting Tools & Technology
The pendulum has swung in sports performance over the last decade or so. Metrics and technology have tilted the teeter totter in exchange for foundational knowledge of first principles such as physiology, psychology, biomechanics, sport competency and program design. Many times, the former without the latter may be a mindless pursuit. Being able to communicate metrics and technology may be more important than the shiny gadget itself (Yes, valid, and reliable measures are important). Placebo or not, this in an important component in the coach’s toolbox. Feelings > Figures > Technology. Twentieth century entrepreneur and economist Roger Babson spoke of the Wall Street market crash in 1929. Prior to the crash another famous economist, statistician and inventor Irving Fisher stated that the stock market had reached "a permanently high plateau". Fisher lost a fortune in the crash (an estimated $10 million, in 1930 dollars!), and he never, thereafter, got out of debt. Babson stated, “He thinks the world is ruled by figures instead of feelings.” Take home point, the world of sports performance is ruled by feelings (trust) not machines. You can acquire all the fancy technology that money can buy, but you better be able to explain to the coach, athlete, and front office staff why it’s important, and how it will drive results, whether input, output, or outcome. The perception may be more important than the measure.
Technology Acquisition
The first two questions I ask prior to procuring a said technology is: Is it valid? Is it reliable?
Validity: the degree to which it measures what it claims it measure
There are several different kinds of validity, but for most coaches with an extensive foundational knowledge of first principles, face validity will do.
Face Validity: does it “look valid” (to all, including untrained observers)?
“Awed or baffled by arcane vocabulary and statistics, clinical investigators may have neglected the appraisal of face validity-a statistically unmeasurable attribute that refers to the measurements clinical sensibility or common sense in doing its intended job.” - Alvan Feinstien
Reliability: the degree to which the measure is free from measurement error
If I’m looking at pre-existing literature, I look for previous studies examining technology/metrics that include both relative reliability and absolute reliability.
“Relative reliability concerns the degree to which individuals maintain their position in a sample with repeated measurements. We assessed this type of reliability using the intraclass correlation coefficient.” (1)
“Absolute reliability is the degree to which repeated measurements vary for individuals, and we expressed this type of reliability with the standard error of measurement expressed in absolute terms (SEM) or as coefficient of variation (CV).” (1)
The 3 P’s
After identifying validly and reliability, my thought process shifts to answering the 3 P’s: Problem, Process, and Presentation. The goal, pending each professionals’ environmental constraints, is to be able to answer these questions in a constructive fashion (Special thanks to my friend Fergus Connolly for his input).
1. Impellizzeri F, Rampinini E, Castagna C, Bishop D, Bravo DF, Tibaudi A, and Wisloff U. Validity of a repeated-sprint test for football. Int J Sports Med 29: 899-905, 2008.
Dashboards, Box Plots and Longitudinal Data
“What’s a good number Coach?” The reality is that’s not always an easy question to answer. I spent my early coaching years guilty of not consistently keeping meticulous records. Sure, we tracked real-time efforts, such as load, set and reps, but I failed to place historical and amalgamated information into longitudinal information steams. In sports science and coaching the most pertinent data we can track is our unique sample. In essence our sample is our population, and our population is our sample (this is not always the case in experimental research design). Experience comes at the user’s expense. My advice to young coaches, keep diligent records.
“My record keeping was comprehensive but really no different from that of a banker who accounts for every penny and can show you the records of transactions going back years and years.” - John Wooden
Longitudinal Data sits atop the “Evidential Substantiation” list for performance coaches.
What kind of Records?
Certainly, this answer will vary pending situation, context, and environment. At the professional level, athlete management systems (AMS) can serve to house, amalgamate, and display data to key decision makers and players alike. AMS can serve as a plug and play tool to adequately store and display relevant information. In the private sector budgetary constraints may seek practitioners to pursue alternative measures. I strongly recommend dashboard tools from fellow colleagues Adam Virgile and Dave McDowell as both of these individuals have made massive contributions to the way we display, collect and communicate our data to relevant coaches, teams and players.
Regarding our teams, we like to have a total of three dashboards/visuals handy. These dashboards provide different narratives to different individuals within the organization.
1) Team Dashboard (For the Coach):
Top 5/Bottom 5
Positional Average
Team Average
Customized Dates
2) Personal Player Card (For the Player):
KPI Tracking
Trends
Radar Chart (Athlete vs Team Standard)
3) Longitudinal Data: (Internal Record Keeping)
Team/Position Over time
I’d like to focus on #3. Pertaining to longitudinal data display, I really like the Box and Whisker Plot. Weather comparing teams, or by position, the box and whisker plot provides a fantastic pictorial narrative. Here’s a quick synopsis:
Box and Whisker Plots are an excellent visual display of the data to compare teams (Row 1), or positions (Row 2) over time.
The range of scores from the lower to upper quartile is the interquartile range. The box (aka the interquartile range) represents the middle 50% of the data set.
The middle line inside each box in the median of the data set. The median is not affected by outliers (the mean of the data set is affected by outliers). The median cuts the box in half (25% of the date on each side)
The “X” represents the mean of the data set
The lower whisker represents the bottom 25% of the data set
The upper whisker represents the bottom 25% of the data set
Outliers are marked with dots (above or below)
Box Plot interpretations:
Smaller boxes = < variability in scores
Longer boxes = > variability in scores
One box is much higher than another = difference b/w groups or positions
Different median distributions can be used to compare groups/positions
Here is how to create a box and whisker plot in Excel
Bottom line, collect longitudinal data over time. Track sample size, amalgamate and display in a way that’s easy to understand. “What’s good Coach?” is not always an easy question to answer. Metrics change, technology changes, but record keeping should not. The best we can do as practitioners is to manage, collect and communicate.
Methods, Descriptive Statistics, and the Texas Sharpshooter
If there’s one thing that my PhD studies taught me as a 40-year-old student, it’s that methods matter. In fact, the methods section of a research article is the most important. The methods section describes what the researchers did, how they collected the information, and the statistical analysis used to provide the inference(s). As Ben Goldacre states in his book Bad Science “Always read the methods and results section of a trial to decide what the findings are, because the discussion and conclusion pages at the end are like the comment pages in a newspaper.” We as spots performance professionals rarely, if ever, perform experimental research in the academic sense. Typically, experimental research encompasses randomization, tight internal and external validity, laboratory set up, and the use of inferential statistics.
Randomization: Random assignments of group and treatment to avoid sampling bias.
Internal Validity: Establishing internal validity involves assessing data collection procedures, reliability and validity of the data, experimental design, and the setting of the experiment.
External Validity: External validity relates to the ability to generalize the results to a larger population. To assess external validity, evaluate the similarities between the lab environment and the real world such as:
People
Places
Times
Order
Materials
Raters/Coaches
Instructions
Conditions
Methods
Inferential Statistics: This is hypothesis testing 101 where we use a representative sample to infer our results to a larger population. Sample → Population. (ANOVA, T-Tests, F-Tests etc.)
In the lab environment the same people, the same places, the same time of day, same ordering, materials, raters, etc. are used to avoid bias, and confounding. The reality is, that is extremely difficult for most coaches who have small staffs, minimal time with their athletes, logistical issues and budgets that may look like my hairline (I’m bald).
Real World vs. Academia
We as sport performance coaches are observational researchers. We observe the athletes in their most natural setting (weight room, ice, field, court). Certainly, our goals are to have high internal validity (tools with reliable and valid measure), but our research design in much different. We don’t have a control group. In Sports Science our sample is our team, and our team is our sample. We use descriptive statistics to weave our narratives while informing players and coaches. We are Texas Sharpshooters.
Descriptive Statistics: A means of describing a data set (in this case, the team). These include:
Central tendency (mean, median, mode)
Measures of dispersion (variance, standard deviation)
Measures of correlation (Pearson’s correlation coefficient)
The Texas sharpshooter: Sports science professionals are essentially Texas sharpshooters. As the story goes, a Texan shoots the side of a barn at random multiple times, looks at the trends and patterns of bullet holes, and then draws the bullseye. This is the equivalent in structured experimental research of HARKing (hypothesizing after the results are known). It’s a big no, no!!! However, in the landscape of applied intervention in sport, it’s how we operate in a daily basis, except for outliers (which are very important). It’s not that we’re shooting at random, it’s the fact that we’re drawing the bullseye after the fact.
The Texas Sharpshooter
Traditional Experimental Research: Hypothesis → Data Collection → Results
Sports Science: Collect Data → Assess Trends → Hypothesize
Do Coaches need to Understand statistics?
Yes and no, and it depends. I believe having a basic understanding of descriptive statistics is very important for coaches to aquire. It allows one to communicate more effectively and perhaps more importantly it enhances the ability for coaches to display the data more effectively (i.e., choosing the correct plot/graph/display). A picture is worth a thousand words. Regarding inferential statistics, if the goal is to be able to read, understand and digest research more efficiently a basic understanding of how hypothesis testing works can work wonders. Published is not synonymous with proven. A base level understanding of inferential statistics can work wonders for building a more robust skeptical thinking filter. Methods matter, and understanding their limitations is a powerful tool in the critical thinkers toolbox.
“Science may be described as the art of systematic over-simplification.” -Karl Popper