The BaselineAPS™ Method From Academia to Silicon Valley Lab to Software
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The BaselineAPS™ Method From Academia to Silicon Valley Lab to Softwarefor the Athlete
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Within high performance cultures, the evaluation of two variables: 1) performance, and 2) load management data currently shape the landscape informing the athletes’  training and wellness programs. (29,31) 

These programs have evolved with a number of new data sources, creating athlete training that is more customized. (28)  Despite adding more data to these two areas of monitoring, the rates of injury have persisted and in some cases increased. (1,2,3,5,9,10)  We believe this is because high performance cultures are missing a third variable: evaluating Load Capacity™.   We define Load Capacity as the capacity for an athlete to accommodate training and performance load based on their movement mechanics.(12,16,17,20,26,29,33,38,39,40,41,42,44,45,46,47,51)

Monitoring an athlete’s volume and intensity (28,29,31) without understanding the underlying mechanics supporting the movement - their Load Capacity. requires assumptions as to whether load monitoring (volume and intensity) and sport specific demands will have an impact on injury or performance. The ability to understand and objectively quantify Load Capacity not only reduces that risk, but allows teams and athletes to safely increase load management (volume and intensity) training loads and ultimately enhance performance training variables with greater confidence. (20,26,38,42,45,47,51) 

Guided by science, evidence based, BaselineAPS uses systematic processes to  identify your players’ foundational readiness with regard to resiliency and performance potential.

Guided by science, evidence based, BaselineAPS uses systematic processes to  identify your players’ foundational readiness with regard to resiliency and performance potential.

BaselineAPS

BaselineAPS is a software platform that uses kinetic data input(31,41,61,62,63,64) from a range of movements (On field or controlled environments) to (a) evaluate the mechanical efficiency of the movement tested, (b) produce results in a comparative scoring system and (c) provides supplemental deep practice programming to target areas of intervention.

Our software BaselineAPS has a strong scientific base in motor learning principles, biomechanics, neuromuscular systems and motor control theory (16,17,18,19,20,31,32,33,38,39,40,41,46,51) uses proprietary patent pending algorithms (ML) and is matched with years 13 years of high performance athlete data.  Our supportive deep practice programming is also backed by neuromuscular, motor learning and motor control principles (16,17,18,19,26,32,38,40,41,46,49,51,67,68)  in addition to the principles of adaptation to training (67,68)

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  • The unique aspect of BaselineAPS is that it provides a profile of your athletes Load Capacity and foundational readiness.  

Load Capacity

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  • We define Load Capacity as:
  • Analyzing an athlete's body structure and movement and the capacity for that structure to accommodate training and performance load (12,20,33,37,60,61).  Biomechanical, neuromuscular systems, motor learning and motor control principles influence one's load capacity (3,25,26,30,32,33,34,38,40,41,42,46,47,56,62). Diminished integration of these systems leads to deleterious movement mechanics and therefore increase the risk for injury potential (17,25,26,33,34,36,38,39,60,61,62,) and diminish performance economy (12,13,14,15,16,17,18,19 20,21,40)
  • How we evaluate Load Capacity:
  • BaselineAPS uses screening modules where the athletes movement is acquired and then categorized relative to deviations from an optimal efficiency (16,19,21,22,38,39,46) and optimal movement quotient.  The optimal movement quotient is one considered to promote the least risk of injury and highest potential for performance efficiency. (16,17,19,20,21,22,38,39,40,41,42,43)

Key Mechanical IndicatorsTM

Our software uses proprietary patent pending algorithms to convert quantitative and qualitative data to a scale value range.  The closer to optimal movement efficiency the outcome, the closer to the optimal scale value (22,28,29,30,31,38,41,43).  Our quantification contributes to 11 Key Mechanical Indicators essentially separating  “signal from noise”.  The outcome produces 11 KMI Quotients which identify strengths and weaknesses in the athletes foundational readiness for movement. (15,16,20,22,29,31,32,33,40,41,42,43,44,45,46,47,48,49,56,61,62)

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Baseline QuotientTM

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  • BaselineAPS uses the 11 KMI Quotients to produce the Baseline Quotient, an overall quantified indication of the athletes Load Capacity
  • The athlete's Baseline is a measure of foundational readiness. It is a final aggregate quantification of an athlete. This score takes into account other factors outside of kinematic measurements, such as strength, balance, flexibility, joint mobility, age, gender, sport and position: the variables that make each athlete unique. The Baseline Quotient enables you to compare athletes in a cohort, understanding who is improving movement efficiency and those who are presenting more likely as a risk towards injury.
  • Everyone has a baseline…what's your team's foundational readiness?

Athlete Classification

When BaselineAPS looks at the results produced from the software and algorithms, it places the athlete’s current preparedness for sport onto a continuum, ranging from Preparation to Optimization to Performance to skill .  Each athlete may have deep practice and targeted exercises focused in any of the areas of the continuum, and after retests, those exercise focus areas typically change.  The goal is to align the exercise regimen with what the athlete and their Load Capacity can handle, all with an eye towards improving the athlete, while also protecting them.

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Athlete Progression

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  • Becoming and remaining a high level athlete is an ongoing journey, and it doesn’t stop.  Your body, strength, quickness, agility, and endurance all change and progress throughout your career.  The BaselineAPS programming personalizes where you are, and where you have come from. It progresses you based upon data including your Athlete Classification, and moves you along the continuum of load capacity and movement optimization. The exercise regimen prepares you to be ready for more strength, efficiency, and performance.
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Doug- Why This Matters

I was labeled as “injury prone” and “not capable of playing through pain” when I was in high school. This led me to lose out on playing basketball in college, and potentially beyond. Seeking ways to help other athletes improve their structural integrity and overall performance has become a lifelong goal of mine.

Interested in improving athlete wellness and performance?
We currently work with multiple high performance cultures on the professional and college level.
Science driven and evidence based, BaselineAPS has fostered confidence through substantiated results.
Want to see your team’s Baseline?

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Our patent-pending method was developed over several years, and informed by many sources, including these below:

J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
H. Katagiri et. al. (2023) Epidemiology of MRI-detected muscle injury in athletes participating in the Tokyo 2020 Olympic Games. BJSM.
M. Walden et. al. (2016) ACL injuries in men’s professional football: a 15 year prospective study on time trends and return-to-play rates. BJSM.
DA. Padua et. al. (2018) National Athletic Trainers’ Association Position Statement: Prevention of Anterior Cruciate Ligament Injury. J. Athl. Training.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
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J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
H. Katagiri et. al. (2023) Epidemiology of MRI-detected muscle injury in athletes participating in the Tokyo 2020 Olympic Games. BJSM.
M. Walden et. al. (2016) ACL injuries in men’s professional football: a 15 year prospective study on time trends and return-to-play rates. BJSM.
DA. Padua et. al. (2018) National Athletic Trainers’ Association Position Statement: Prevention of Anterior Cruciate Ligament Injury. J. Athl. Training.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
J. Elkstrand, Armin Spreco, Hakan Bengtsson, Ronald Bahr (2021) Injury rates in men’s progessional football: an 18 year prospective cohort study of almost 12000 injuries sustained during 1.8 million hours of play. BJSM.
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